Methods and systems for managing crop harvesting activities

ABSTRACT

A computer-implemented method for managing crop harvesting activities is implemented by a harvest advisor computing device in communication with a memory. The method includes receiving an initial date of a crop within a field, receiving an initial moisture value associated with the crop and the initial date, and receiving a target harvest moisture value associated with the crop. The method also includes receiving field condition data associated with the field. The method further includes computing, by the harvest advisor, a target harvest date for the crop based at least in part on the initial date, the initial moisture value, the field condition data, and the target harvest moisture value, and displaying the target harvest date for the crop to the grower for harvest planning. The target harvest date indicates a date at which the crop will have a present moisture value approximately equal to the target harvest moisture value.

BACKGROUND

The embodiments described herein relate generally to agriculturalactivities and, more particularly, systems and methods for managing andrecommending agricultural activities at the field level based oncrop-related data and field-condition data.

Agricultural production requires significant strategy and analysis. Inmany cases, agricultural growers (e.g., farmers or others involved inagricultural cultivation) are required to analyze a variety of data tomake strategic decisions months in advance of the period of cropcultivation (i.e., growing season). In making such strategic decisions,growers must consider at least some of the following decisionconstraints: fuel and resource costs, historical and projected weathertrends, soil conditions, projected risks posed by pests, disease andweather events, and projected market values of agricultural commodities(i.e., crops). Analyzing these decision constraints may help a grower topredict key agricultural outcomes including crop yield, energy usage,cost and resource utilization, and farm profitability. Such analysis mayinform a grower's strategic decisions of determining crop cultivationtypes, methods, and timing.

Despite its importance, such analysis and strategy is difficult toaccomplish for a variety of reasons. First, obtaining reliableinformation for the various considerations of the grower is oftendifficult. Second, aggregating such information into a usable manner isa time consuming task. Third, where data is available, it may not beprecise enough to be useful to determine strategy. For example, weatherdata (historical or projected) is often generalized for a large regionsuch as a county or a state. In reality, weather may vary significantlyat a much more granular level, such as an individual field. In addition,terrain features may cause weather data to vary significantly in evensmall regions.

Additionally, growers often must regularly make decisions during growingseason. Such decisions may include adjusting when to harvest, providingsupplemental fertilizer, and how to mitigate risks posed by pests,disease and weather. As a result, growers must continually monitorvarious aspects of their crops during the growing season includingweather, soil, and crop conditions. Accurately monitoring all suchaspects at a granular level is difficult and time consuming.Accordingly, methods and systems for analyzing crop-related data andproviding field condition data and strategic recommendations formaximizing crop yield are desirable.

BRIEF DESCRIPTION OF THE DISCLOSURE

In one aspect, a computer-implemented method for managing cropharvesting activities is provided. The method is implemented by aharvest advisor computing device in communication with a memory. Themethod includes receiving, by the harvest advisor computing device, aninitial date of a crop within a field. The method also includesreceiving, by the harvest advisor computing device, an initial moisturevalue associated with the crop and the initial date. The method furtherincludes receiving, by the harvest advisor computing device, a targetharvest moisture value associated with the crop. The method alsoincludes receiving, by the harvest advisor computing device, fieldcondition data associated with the field. The method further includescomputing, by the harvest advisor computing device, a target harvestdate for the crop based at least in part on the initial date, theinitial moisture value, the field condition data, and the target harvestmoisture value. The target harvest date indicates a date at which thecrop will have a present moisture value approximately equal to thetarget harvest moisture value. The method also includes displaying thetarget harvest date for the crop to the grower for harvest planning.

In another aspect, a harvest advisor computing device for managing cropharvesting activities is provided. The harvest advisor computing deviceincludes at least one processor in communication with a memory. Theprocessor is configured to receive an initial date of a crop within afield. The processor is also configured to receive an initial moisturevalue associated with the crop and the initial date. The processor isfurther configured to receive a target harvest moisture value associatedwith the crop. The processor is also configured to receive fieldcondition data associated with the field. The processor is furtherconfigured to compute a target harvest date for the crop based at leastin part on the initial date, the initial moisture value, the fieldcondition data, and the target harvest moisture value. The targetharvest date indicates a date at which the crop will have a presentmoisture value approximately equal to the target harvest moisture value.The processor is also configured to display the target harvest date forthe crop to the grower for harvest planning.

In an additional aspect, computer-readable storage media for managingagricultural activities having computer-executable instructions embodiedthereon are provided. When executed by at least one processor, thecomputer-executable instructions cause the processor to receive aninitial date of a crop within a field. The computer-executableinstructions also cause the processor to receive an initial moisturevalue associated with the crop and the initial date. Thecomputer-executable instructions further cause the processor to receivea target harvest moisture value associated with the crop. Thecomputer-executable instructions also cause the processor to receivefield condition data associated with the field. The computer-executableinstructions further cause the processor to compute a target harvestdate for the crop based at least in part on the initial date, theinitial moisture value, the field condition data, and the target harvestmoisture value. The target harvest date indicates a date at which thecrop will have a present moisture value approximately equal to thetarget harvest moisture value. The computer-executable instructions alsocause the processor to display the target harvest date for the crop tothe grower for harvest planning.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram depicting an example agricultural environmentincluding a plurality of fields that are monitored and managed with anagricultural intelligence computer system that is used to manage andrecommend agricultural activities;

FIG. 2 is a block diagram of a user computing device, used for managingand recommending agricultural activities, as shown in the agriculturalenvironment of FIG. 1;

FIG. 3 is a block diagram of a computing device, used for managing andrecommending agricultural activities, as shown in the agriculturalenvironment of FIG. 1;

FIG. 4 is an example data flowchart of managing and recommendingagricultural activities using the computing devices of FIGS. 1, 2, and 3in the agricultural environment shown in FIG. 1;

FIG. 5 is an example method for managing agricultural activities in theagricultural environment of FIG. 1;

FIG. 6 is an example method for recommending agricultural activities inthe agricultural environment of FIG. 1;

FIG. 7 is a diagram of an example computing device used in theagricultural environment of FIG. 1 to recommend and manage agriculturalactivities; and

FIGS. 8-30 are example illustrations of information provided by theagricultural intelligence computer system of FIG. 3 to the user deviceof FIG. 2 to facilitate the management and recommendation ofagricultural activities.

FIG. 31 is a screenshot of an example field management interface thatmay be used for managing crop harvesting activities for one or morefields of the user shown in FIG. 1.

FIG. 32 is a screenshot of an example harvest advisor interfacepresented to the user shown in FIG. 1 by the harvest advisor computingdevice shown in FIG. 1 for managing crop harvesting activities.

FIG. 33 is a screenshot of an example harvest advisor interface that maybe used by the user 110 shown in FIG. 1 for managing crop harvestingactivities.

Referring now to FIG. 34, method is an example method for managing cropharvesting activities.

Although specific features of various embodiments may be shown in somedrawings and not in others, this is for convenience only. Any feature ofany drawing may be referenced and/or claimed in combination with anyfeature of any other drawing.

DETAILED DESCRIPTION OF THE DISCLOSURE

The following detailed description of the embodiments of the disclosurerefers to the accompanying drawings. The same reference numbers indifferent drawings may identify the same or similar elements. Also, thefollowing detailed description does not limit the claims.

The subject matter described herein relates generally to managing andrecommending agricultural activities for a user such as a grower or afarmer. Specifically, a first embodiment of the methods and systemsdescribed herein includes (i) receiving a plurality of field definitiondata, (ii) retrieving a plurality of input data from a plurality of datanetworks, (iii) determining a field region based on the field definitiondata, (iv) identifying a subset of the plurality of input dataassociated with the field region, (v) determining a plurality of fieldcondition data based on the subset of the plurality of input data, and(vi) providing the plurality of field condition data to the user device.

A second embodiment of the methods and systems described herein includes(i) receiving a plurality of field definition data, (ii) retrieving aplurality of input data from a plurality of data networks, (iii)determining a field region based on the field definition data, (iv)identifying a subset of the plurality of input data associated with thefield region, (v) determining a plurality of field condition data basedon the subset of the plurality of input data, (vi) identifying aplurality of field activity options, (vii) determining a recommendationscore for each of the plurality of field activity options based at leastin part on the plurality of field condition data, and (viii) providing arecommended field activity option from the plurality of field activityoptions based on the plurality of recommendation scores.

As described herein, one of the example embodiments includes variousadvisor modules that are configured to provide certain recommendationservices to the user (e.g., a grower). One of the advisors is referredto herein as a harvest advisor. As described in further detail below,the agricultural intelligence computer system may be in communicationwith the harvest advisor computing module such that the harvest advisorcomputing module is configured to: (i) receive an initial date, such asa maturity date or “black layer date”, of a crop growing within a field;(ii) receive an initial moisture value associated with the crop and theinitial date (a “starting” moisture percentage of the crop, e.g., themoisture percentage of a corn crop on the black layer date); (iii)receive a target harvest moisture value associated with the crop (e.g.,the grower may identify at what moisture value he would prefer toharvest the crop); (iv) receive field condition data associated with thefield, such as known or projected precipitation, wind, and temperaturevalue at the field; (v) compute a target harvest date for the crop(i.e., a recommended date when to harvest the crop such that themoisture of the crop is approximately the target harvest moisture value)based at least in part on the initial date, the initial moisture value,the field condition data, and the target harvest moisture value, whereinthe target harvest date indicates a date at which the crop will have apresent moisture value approximately equal to the target harvestmoisture value; and (vi) display the target harvest date for the crop tothe grower for harvest planning.

In at least some agricultural environments (e.g., farms, groups offarms, and other agricultural cultivation environments), agriculturalgrowers employ significant strategy and analysis to make decisions onagricultural cultivation. In many cases, growers analyze a variety ofdata to make strategic decisions months in advance of the period of cropcultivation (i.e., growing season). In making such strategic decisions,growers must consider at least some of the following decisionconstraints: fuel and resource costs, historical and projected weathertrends, soil conditions, projected risks posed by pests, disease andweather events, and projected market values of agricultural commodities(i.e., crops). Analyzing these decision constraints may help a grower topredict key agricultural outcomes including crop yield, energy usage,cost and resource utilization, and farm profitability. Such analysis mayinform a grower's strategic decisions of determining crop cultivationtypes, methods, and timing. Despite its importance, such analysis andstrategy is difficult to accomplish for a variety of reasons. First,obtaining reliable information for the various considerations of thegrower is often difficult. Second, aggregating such information into ausable manner is a time consuming task. Third, where data is available,it may not be precise enough to be useful to determine strategy. Forexample, weather data (historical or projected) is often generalized fora large region such as a county or a state. In reality, weather may varysignificantly at a much more granular level, such as an individualfield. Terrain features may cause weather data to vary significantly ineven small regions.

Additionally, growers often must regularly make decisions during growingseason. Such decisions may include adjusting when to harvest, providingsupplemental fertilizer, and how to mitigate risks posed by pests,disease and weather. As a result, growers must continually monitorvarious aspects of their crops during the growing season includingweather, soil, and crop conditions. Accurately monitoring all suchaspects at a granular level is difficult and time consuming.Accordingly, methods and systems for analyzing crop-related data, andproviding field condition data and strategic recommendations formaximizing crop yield are desirable. Accordingly, the systems andmethods described herein facilitate the management and recommendation ofagricultural activities to growers.

As used herein, the term “agricultural intelligence services” refers toa plurality of data providers used to aid a user (e.g., a farmer,agronomist or consultant) in managing agricultural services and toprovide the user with recommendations of agricultural services. As usedherein, the terms “agricultural intelligence service”, “data network”,“data service”, “data provider”, and “data source” are usedinterchangeably herein unless otherwise specified. In some embodiments,the agricultural intelligence service may be an external data network(e.g., a third-party system). As used herein, data provided by any such“agricultural intelligence services” or “data networks” may be referredto as “input data”, or “source data.”

As used herein, the term “agricultural intelligence computer system”refers to a computer system configured to carry out the methodsdescribed herein. The agricultural intelligence computer system is innetworked connectivity with a “user device” (e.g., desktop computer,laptop computer, smartphone, personal digital assistant, tablet or othercomputing device) and a plurality of data sources. In the exampleembodiment, the agricultural intelligence computer system provides theagricultural intelligence services using a cloud-based software as aservice (SaaS) model. Therefore, the agricultural intelligence computersystem may be implemented using a variety of distinct computing devices.The user device may interact with the agricultural intelligence computersystem using any suitable network.

In an example embodiment, an agricultural machine (e.g., combine,tractor, cultivator, plow, subsoiler, sprayer or other machinery used ona farm to help with farming) may be coupled to a computing device(“agricultural machine computing device”) that interacts with theagricultural intelligence computer system in a similar manner as theuser device. In some examples, the agricultural machine computing devicecould be a planter monitor, planter controller or a yield monitor. Theagricultural machine and agricultural machine computing device mayprovide the agricultural intelligence computer system with fielddefinition data and field-specific data.

The term “field definition data” refers to field identifiers, geographicidentifiers, boundary identifiers, crop identifiers, and any othersuitable data that may be used to identify farm land, such as a commonland unit (CLU), lot and block number, a parcel number, geographiccoordinates and boundaries, Farm Serial Number (FSN), farm number, tractnumber, field number, section, township, and/or range. According to theUnited States Department of Agriculture (USDA) Farm Service Agency, aCLU is the smallest unit of land that has a permanent, contiguousboundary, a common land cover and land management, a common owner and acommon producer in agricultural land associated with USDA farm programs.CLU boundaries are delineated from relatively permanent features such asfence lines, roads, and/or waterways. The USDA Farm Service Agencymaintains a Geographic Information Systems (GIS) database containingCLUs for farms in the United States.

When field definition and field-specific data is not provided directlyto the agricultural intelligence computer system via one or moreagricultural machines or agricultural machine devices that interactswith the agricultural intelligence computer system, the user may beprompted via one or more user interfaces on the user device (served bythe agricultural intelligence computer system) to input suchinformation. In an example embodiment, the user may identify fielddefinition data by accessing a map on the user device (served by theagricultural intelligence computer system) and selecting specific CLUsthat have been graphically shown on the map. In an alternativeembodiment, the user may identify field definition data by accessing amap on the user device (served by the agricultural intelligence computersystem) and drawing boundaries of the field over the map. Such CLUselection or map drawings represent geographic identifiers. Inalternative embodiments, the user may identify field definition data byaccessing field definition data (provided as shape files or in a similarformat) from the U.S. Department of Agriculture Farm Service Agency orother source via the user device and providing such field definitiondata to the agricultural intelligence computer system. The landidentified by “field definition data” may be referred to as a “field” or“land tract.” As used herein, the land farmed, or “land tract”, iscontained in a region that may be referred to as a “field region.” Sucha “field region” may be coextensive with, for example, temperature gridsor precipitation grids, as used and defined below.

The term “field-specific data” refers to (a) field data (e.g., fieldname, soil type, acreage, tilling status, irrigation status), (b)harvest data (e.g., crop type, crop variety, crop rotation, whether thecrop is grown organically, harvest date, Actual Production History(APH), expected yield, yield, crop price, crop revenue, grain moisture,tillage practice, weather information (e.g., temperature, rainfall) tothe extent maintained or accessible by the user, previous growing seasoninformation), (c) soil composition (e.g., pH, organic matter (OM),cation exchange capacity (CEC)), (d) planting data (e.g., planting date,seed(s) type, relative maturity (RM) of planted seed(s), seedpopulation), (e) nitrogen data (e.g., application date, amount, source),(f) pesticide data (e.g., pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant), (g) irrigation data (e.g.,application date, amount, source), and (h) scouting observations(photos, videos, free form notes, voice recordings, voicetranscriptions, weather conditions (temperature, precipitation (currentand over time), soil moisture, crop growth stage, wind velocity,relative humidity, dew point, black layer)). If field-specific data isnot provided via one or more agricultural machines or agriculturalmachine devices that interacts with the agricultural intelligencecomputer system in a similar manner as the user device, a user mayprovide such data via the user device to the agricultural intelligencecomputer system. In other words, the user accesses the agriculturalintelligence computer system via the user device and provides thefield-specific data.

The agricultural intelligence computer system also utilizesenvironmental data to provide agricultural intelligence services. Theterm “environmental data” refers to environmental information related tofarming activities such as weather information, vegetation and cropgrowth information, seed information, pest and disease information andsoil information. Environmental data may be obtained from external datasources accessible by the agricultural intelligence computer system.Environmental data may also be obtained from internal data sourcesintegrated within the agricultural intelligence computer system. Datasources for environmental data may include weather radar sources,satellite-based precipitation sources, meteorological data sources(e.g., weather stations), satellite imagery sources, aerial imagerysources (e.g., airplanes, unmanned aerial vehicles), terrestrial imagerysources (e.g., agricultural machine, unmanned terrestrial vehicle), soilsources and databases, seed databases, crop phenology sources anddatabases, and pest and disease reporting and prediction sources anddatabases. For example, a soil database may relate soil types and soillocations to soil data including pH levels, organic matter makeups, andcation exchange capacities. Although in many examples, the user mayaccess data from data sources indirectly via the agriculturalintelligence computer system, in other examples, the user may directlyaccess the data sources via any suitable network connection.

The agricultural intelligence computer system processes the plurality offield definition data, field-specific data and environmental data from aplurality of data sources to provide a user with the plurality of fieldcondition data for the field or field region identified by the fielddefinition data. The term “field condition data” refers tocharacteristics and conditions of a field that may be used by theagricultural intelligence computer system to manage and recommendagricultural activities. Field condition data may include, for example,and without limitation, field weather conditions, field workabilityconditions, growth stage conditions, soil moisture, and precipitationconditions. Field condition data is presented to the user using the userdevice.

The agricultural intelligence computer system also provides a user witha plurality of agricultural intelligence services for the land tract orfield region identified by the field definition data. Such agriculturalintelligence services may be used to recommend courses of action for theuser to undertake. In an example embodiment, the recommendation servicesinclude a planting advisor, a nitrogen application advisor, a pestadvisor, a field health advisor, a harvest advisor, and a revenueadvisor. Each is discussed herein.

System Architecture

As noted above, the agricultural intelligence computer system may beimplemented using a variety of distinct computing devices using anysuitable network. In an example embodiment, the agriculturalintelligence computer system uses a client-server architectureconfigured for exchanging data over a network (e.g., the Internet). Oneor more user devices may communicate via a network with a userapplication or an application platform. The application platformrepresents an application available on user devices that may be used tocommunicate with agricultural intelligence computer system. Otherexample embodiments may include other network architectures, such aspeer-to-peer or distributed network environment.

The application platform may provide server-side functionality, via thenetwork to one or more user devices. Accordingly, the applicationplatform may include client side software stored locally at the userdevice as well as server side software stored at the agriculturalintelligence computer system. In an example embodiment, the user devicemay access the application platform via a web client or a programmaticclient. The user device may transmit data to, and receive data from oneor more front-end servers. In an example embodiment, the data may takethe form of requests and user information input, such as field-specificdata, into the user device. One or more front-end servers may processthe user device requests and user information and determine whether therequests are service requests or content requests, among other things.Content requests may be transmitted to one or more content managementservers for processing. Application requests may be transmitted to oneor more application servers. In an example embodiment, applicationrequests may take the form of a request to provide field condition dataand/or agricultural intelligence services for one or more fields.

In an example embodiment, the application platform may include one ormore servers in communication with each other. For example, theagricultural intelligence computer system may include front-end servers,application servers, content management servers, account servers,modeling servers, environmental data servers, and correspondingdatabases. As noted above, environmental data may be obtained fromexternal data sources accessible by the agricultural intelligencecomputer system or it may be obtained from internal data sourcesintegrated within the agricultural intelligence computer system.

In an example embodiment, external data sources may include third-partyhosted servers that provide services to the agricultural intelligencecomputer system via Application Program Interface (API) requests andresponses. The frequency at which the agricultural intelligence computersystem may consume data published or made available by these third-partyhosted servers may vary based on the type of data. In an exampleembodiment, a notification may be sent to the agricultural intelligencecomputer system when new data is available by a data source. Theagricultural intelligence computer system may transmit an API call viathe network to the agricultural intelligence computer system hosting thedata and receive the new data in response to the call. To the extentneeded, the agricultural intelligence computer system may process thedata to enable components of the application platform to handle thedata. For example, processing data may involve extracting data from astream or a data feed and mapping the data to a data structure, such asan XML data structure. Data received and/or processed by theagricultural intelligence computer system may be transmitted to theapplication platform and stored in an appropriate database.

When an application request is made, the one or more application serverscommunicate with the content management servers, account servers,modeling servers, environmental data servers, and correspondingdatabases. In one example, modeling servers may generate a predeterminednumber of simulations (e.g., 10,000 simulations) using, in part,field-specific data and environmental data for one or more fieldsidentified based on field definition data and user information.Depending on the type of application request, the field-specific dataand environmental data for one or more fields may be located in thecontent management servers, account servers, environmental data servers,the corresponding databases, and, in some instances, archived in themodeling servers and/or application servers. Based on the simulationsgenerated by the modeling servers, field condition data and/oragricultural intelligence services for one or more fields is provided tothe application servers for transmission to the requesting user devicevia the network. More specifically, the user may use the user device toaccess a plurality of windows or displays showing field condition dataand/or agricultural intelligence services, as described below.

Although the aforementioned application platform has been configuredwith various example embodiments above, one skilled in the art willappreciate that any configuration of servers may be possible and thatexample embodiments of the present disclosure need not be limited to theconfigurations disclosed herein.

Field Condition Data

Field Weather and Temperature Conditions

As part of the field condition data provided, the agriculturalintelligence computer system tracks field weather conditions for eachfield identified by the user. The agricultural intelligence computersystem determines current weather conditions including fieldtemperature, wind, humidity, and dew point. The agriculturalintelligence computer system also determines forecasted weatherconditions including field temperature, wind, humidity, and dew pointfor hourly projected intervals, daily projected intervals, or anyinterval specified by the user. The forecasted weather conditions arealso used to forecast field precipitation, field workability, and fieldgrowth stage. Near-term forecasts are determined using a meteorologicalmodel (e.g., the Microcast model) while long-term projections aredetermined using historical analog simulations.

The agricultural intelligence computer system uses grid temperatures todetermine temperature values. Known research shows that using gridtechniques provides more accurate temperature measurements thanpoint-based temperature reporting. Temperature grids are typicallysquare physical regions, typically 2.5 miles by 2.5 miles. Theagricultural intelligence computer system associates the field with atemperature grid that contains the field. The agricultural intelligencecomputer system identifies a plurality of weather stations that areproximate to the temperature grid. The agricultural intelligencecomputer system receives temperature data from the plurality of weatherstations. The temperatures reported by the plurality of weather stationsare weighted based on their relative proximity to the grid such thatmore proximate weather stations have higher weights than less proximateweather stations. Further, the relative elevation of the temperaturegrid is compared to the elevation of the plurality of weather stations.Temperature values reported by the plurality of weather stations areadjusted in response to the relative difference in elevation. In someexamples, the temperature grid includes or is adjacent to a body ofwater. Bodies of water are known to cause a reduction in the temperatureof an area. Accordingly, when a particular field is proximate to a bodyof water as compared to the weather station providing the temperaturereading, the reported temperature for the field is adjusted downwards toaccount for the closer proximity to the body of water.

Precipitation values are similarly determined using precipitation gridsthat utilize meteorological radar data. Precipitation grids have similarpurposes and characteristics as temperature grids. Specifically, theagricultural intelligence computer system uses available data sourcessuch as the National Weather Service's NEXRAD Doppler radar data, raingauge networks, and weather stations across the U.S. The agriculturalintelligence computer system further validates and calibrates reporteddata with ground station and satellite data. In the example embodiment,the Doppler radar data is obtained for the precipitation grid. TheDoppler radar data is used to determine an estimate of precipitation forthe precipitation grid. The estimated precipitation is adjusted based onother data sources such as other weather radar sources, ground weatherstations (e.g., rain gauges), satellite precipitation sources (e.g., theNational Oceanic and Atmospheric Administration's Satellite Applicationsand Research), and meteorological sources. By utilizing multipledistinct data sources, more accurate precipitation tracking may beaccomplished.

Current weather conditions and forecasted weather conditions (hourly,daily, or as specified by the user) are displayed on the user devicegraphically along with applicable information regarding the specificfield, such as field name, crop, acreage, field precipitation, fieldworkability, field growth stage, soil moisture, and any other fielddefinition data or field-specific data that the user may specify. Suchinformation may be displayed on the user device in one or morecombinations and level of detail as specified by the user.

In an example embodiment, temperature can be displayed as hightemperatures, average temperatures and low temperatures over time.Temperature can be shown during a specific time and/or date range and/orharvest year and compared against prior times, years, including a 5 yearaverage, a 15 year average, a 30 year average or as specified by theuser.

In an example embodiment, precipitation can be displayed as the amountof precipitation and/or accumulated precipitation over time.Precipitation can be shown during a specific time period and/or daterange and/or harvest year and compared against prior times, years,including a 5 year average, a 15 year average, a 30 year average or asspecified by the user. Precipitation can also be displayed as past andfuture radar data. In an example embodiment, past radar may be displayedover the last 1.5 hours or as specified by the user. Future radar may bedisplayed over the next 6 hours or as specified by the user. Radar maybe displayed as an overlay of an aerial image map showing the user's oneor more fields where the user has the ability to zoom in and out of themap. Radar can be displayed as static at intervals selected by the useror continuously over intervals selected by the user. The underlyingradar data received and/or processed by the agricultural intelligencecomputer system may be in the form of Gridded Binary (GRIB) files thatincludes forecast reflectivity files, precipitation type, andprecipitation-typed reflectivity values.

Field Workability Conditions Data

As part of the field condition data, the agricultural intelligencecomputer system provides field workability conditions, which indicatethe degree to which a field or section of a field (associated with thefield definition data) may be worked for a given time of year usingmachinery or other implements. In an example embodiment, theagricultural intelligence computer system retrieves field historicalprecipitation data over a predetermined period of time, field predictedprecipitation over a predetermined period of time, and fieldtemperatures over a predetermined period of time. The retrieved data isused to determine one or more workability index.

In an example embodiment, the workability index may be used to derivethree values of workability for particular farm activities. The value of“Good” workability indicates high likelihood that field conditions areacceptable for use of machinery or a specified activity during anupcoming time interval. The value of “Check” workability indicates thatfield conditions may not be ideal for the use of machinery or aspecified activity during an upcoming time interval. The value of “Stop”workability indicates that field conditions are not suitable for work ora specified activity during an upcoming time interval.

Determined values of workability may vary depending upon the farmactivity. For example, planting and tilling typically require a lowlevel of muddiness and may require a higher workability index to achievea value of “Good” than activities that allow for a higher level ofmuddiness. In some embodiments, workability indices are distinctlycalculated for each activity based on a distinct set of factors. Forexample, a workability index for planting may correlate to predictedtemperature over the next 60 hours while a workability index forharvesting may be correlated to precipitation alone. In some examples,user may be prompted at the user device to answer questions regardingfield activities if such information has not already been provided tothe agricultural intelligence computer system. For example, a user maybe asked what field activities are currently in use. Depending upon theresponse, the agricultural intelligence computer system may adjust itscalculations of the workability index because of the user's activities,thereby incorporating the feedback of the user into the calculation ofthe workability index. Alternately, the agricultural intelligencecomputer system may adjust the recommendations made to the user foractivities. In a further example, the agricultural intelligence computersystem may recommend that the user stop such activities based on theresponses.

Field Growth Stage Conditions

As part of the field condition data provided, the agriculturalintelligence computer system provides field growth stage conditions(e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growthstages) for the crops being grown in each listed field. Vegetativegrowth stages for corn typically are described as follows. The “VE”stage indicates emergence, the “V1” stage indicates a first fullyexpanded leaf with a leaf collar; the “V2” stage indicates a secondfully expanded leaf with the leaf collar; the “V3” stage indicates athird fully expanded leaf with the leaf collar; any “V(n)” stageindicates an nth fully expanded leaf with the leaf collar; and the “VT”stage indicates that the tassel of the corn is fully emerged. In thereproductive growth stage model described, “R1” indicates a silkingperiod in which pollination and fertilization processes take place; the“R2” or blister stage (occurring 10-14 days after R1) indicates that thekernel of corn is visible and resembles a blister; the “R3” or milkstage (occurring 18-22 days after R1) indicates that the kernel isyellow outside and contains milky white fluid; the “R4” or dough stage(occurring 24-28 days after R1) indicates that the interior of thekernel has thickened to a dough-like consistency; the “R5” or dent stage(occurring 35-42 days after R1) indicates that the kernels are indentedat the top and beginning drydown; and the “R6” or physiological maturitystage (occurring 55-65 days after R1) indicates that kernels havereached maximum dry matter accumulation. Field growth stage conditionsmay be used to determine timing of key farming decisions. Theagricultural intelligence computer system computes crop progression foreach crop through stages of growth (agronomic stages) by tracking theimpact of weather (both historic and forecasted) on the phenomenologicaldevelopment of the crop from planting through harvest.

In the example embodiment, the agricultural intelligence computer systemuses the planting date entered by the user device to determine fieldgrowth stage conditions. In other words, the user may enter the plantingdate into the user device, which communicates the planting date to theagricultural intelligence computer system. Alternately, the agriculturalintelligence computer system may estimate the planting date using asystem algorithm. Specifically, the planting date may be estimated basedon agronomic stage data and planting practices in the region associatedwith the field definition data. The planting practices may be receivedfrom a data service such as a university data network that monitorstypical planting techniques for a region. The agricultural intelligencecomputer system further uses data regarding the user's farming practiceswithin the current season and for historical seasons, therebyfacilitating historical analysis. In other words, the agriculturalintelligence computer system is configured to use historical practicesof each particular grower on a subject field or to alternately usehistorical practices for the corresponding region to predict theplanting date of a crop when the actual planting date is not provided bythe grower. The agricultural intelligence computer system determines arelative maturity value of the crops based on expected heat units overthe growing season in light of the planting date, the user's farmingpractices, and field-specific data. As heat is a proxy for energyreceived by crops, the agricultural intelligence computer systemcalculates expected heat units for crops and determines a development ofmaturity of the crops. In the example embodiment, maximum temperaturesand low temperatures are used to estimate heat units.

Soil Moisture

As part of the field condition data, the agricultural intelligencecomputer system determines and provides soil moisture data via a displayshowing a client application on the user device. Soil moisture indicatesthe percent of total water capacity available to the crop that ispresent in the soil of the field. Soil moisture values are initializedat the beginning of the growing season based on environmental data inthe agricultural intelligence computer system at that time, such as datafrom the North American Land Data Assimilation System, andfield-specific data. In another embodiment, a soil analysis computingdevice may analyze soil samples from a plurality of fields for a growerwherein the plurality of fields includes a selected field. Onceanalyzed, the results may be directly provided from the soil analysiscomputing device to the agricultural intelligence computer system sothat the soil analysis results may be provided to the grower. Further,data from the soil analysis may be inputted into the agriculturalintelligence computer system for use in determining field condition dataand agricultural intelligence services.

Soil moisture values are then adjusted, at least daily, during thegrowing season by tracking moisture entering the soil via precipitationand moisture leaving the soil via evapotranspiration (ET).

In some examples, water that is received in an area as precipitationdoes not enter the soil because it is lost as run off. Accordingly, inone example, a gross and net precipitation value is calculated. Grossprecipitation indicates a total precipitation value. Net precipitationexcludes a calculated amount of water that never enters the soil becauseit is lost as runoff. A runoff value is determined based on theprecipitation amount over time and a curve determined by the USDAclassification of soil type. The systems account for a user's specificfield-specific data related to soil to determine runoff and the runoffcurve for the specific field. Soil input data, described above, mayalternately be provided via the soil analysis computing device. Lighter,sandier soils allow greater precipitation water infiltration andexperience less runoff during heavy precipitation events than heavier,more compact soils. Heavier or denser soil types have lowerprecipitation infiltration rates and lose more precipitation to runoffon days with large precipitation events.

Daily evapotranspiration associated with a user's specific field iscalculated based on a version of the standard Penman-Monteith ET model.The total amount of water that is calculated as leaving the soil throughevapotranspiration on a given day is based on the following:

-   -   1. Maximum and minimum temperatures for the day: Warmer        temperatures result in greater evapotranspiration values than        cooler temperatures.    -   2. Latitude: During much of the corn growing season, fields at        more northern latitudes experience greater solar radiation than        fields at more southern latitudes due to longer days. But fields        at more northern latitudes also get reduced radiation due to        earth tilting. Areas with greater net solar radiation values        will have relatively higher evapotranspiration values than areas        with lower net solar radiation values.    -   3. Estimated crop growth stage: Growth stages around pollination        provide the highest potential daily evapotranspiration values        while growth stages around planting and late in grain fill        result in relatively lower daily evapotranspiration values,        because the crop uses less water in these stages of growth.    -   4. Current soil moisture: The agricultural intelligence computer        system's model accounts for the fact that crops conserve and use        less water when less water is available in the soil. The        reported soil moisture values reported that are above a certain        percentage, determined by crop type, provide the highest        potential evapotranspiration values and potential        evapotranspiration values decrease as soil moisture values        approach 0%. As soil moisture values fall below this percentage,        corn will start conserving water and using soil moisture at less        than optimal rates. This water conservation by the plant        increases as soil moisture values decrease, leading to lower and        lower daily evapotranspiration values.    -   5. Wind: Evapotranspiration takes into account wind; however,        evapotranspiration is not as sensitive to wind as to the other        conditions. In an example embodiment, a set wind speed of 2        meters per second is used for all evapotranspiration        calculations.

Alerts and Reporting

The agricultural intelligence computer system is additionally configuredto provide alerts based on weather and field-related information.Specifically, the user may define a plurality of thresholds for each ofa plurality of alert categories. When field condition data indicatesthat the thresholds have been exceeded, the user device will receivealerts. Alerts may be provided via the application (e.g., notificationupon login, push notification), email, text messages, or any othersuitable method. Alerts may be defined for crop cultivation monitoring,for example, hail size, rainfall, overall precipitation, soil moisture,crop scouting, wind conditions, field image, pest reports or diseasereports. Alternately, alerts may be provided for crop growth strategy.For example, alerts may be provided based on commodity prices, grainprices, workability indexes, growth stages, and crop moisture content.In some examples, an alert may indicate a recommended course of action.For example, the alert may recommend that field activities (e.g.,planting, nitrogen application, pest and disease treatment, irrigationapplication, scouting, or harvesting) occur within a particular periodof time. The agricultural intelligence computer system is alsoconfigured to receive information on farming activities from, forexample, the user device, an agricultural machine and/or agriculturalmachine computing device, or any other source. Accordingly, alerts mayalso be provided based on logged farm activity such as planting,nitrogen application, spraying, irrigation, scouting, or harvesting. Insome examples, alerts may be provided regardless of thresholds toindicate certain field conditions. In one example, a dailyprecipitation, growth stage, field image or temperature alert may beprovided to the user device.

The agricultural intelligence computer system is further configured togenerate a plurality of reports based on field condition data. Suchreports may be used by the user to improve strategy and decision-makingin farming. The reports may include reports on crop growth stage,temperature, humidity, soil moisture, precipitation, workability, pestrisk, and disease risk. The reports may also include one or more fielddefinition data, environmental data, field-specific data, scouting andlogging events, field condition data, summary of agriculturalintelligence services or FSA Form 578.

Scouting and Notes

The agricultural intelligence computer system is also configured toreceive supplemental information from the user device. For example, auser may provide logging or scouting events regarding the fieldsassociated with the field definition data. The user may access a loggingapplication at the user device and update the agricultural intelligencecomputer system. In one embodiment, the user accesses the agriculturalintelligence computer system via a user device while being physicallylocated in a field to enter field-specific data. The agriculturalintelligence computer system might automatically display and transmitthe date and time and field definition data associated with thefield-specific data, such as geographic coordinates and boundaries. Theuser may provide general data for activities including field, location,date, time, crop, images, and notes. The user may also provide dataspecific to particular activities such as planting, nitrogenapplication, pesticide application, harvesting, scouting, and currentweather observations. Such supplemental information may be associatedwith the other data networks and used by the user for analysis.

The agricultural intelligence computer system is additionally configuredto display scouting and logging events related to the receipt offield-specific data from the user via one or more agricultural machinesor agricultural machine devices that interacts with the agriculturalintelligence computer system or via the user device. Such informationcan be displayed as specified by the user. In one example, theinformation is displayed on a calendar on the user device, wherein theuser can obtain further details regarding the information as necessary.In another example, the information is displayed in a table on the userdevice, wherein the user can select the specific categories ofinformation that the user would like displayed.

The agricultural intelligence computer system also includes (or is indata communication with) a plurality of modules configured to analyzefield condition data and other data available to the agriculturalintelligence computer system and to recommend certain agriculturalactions (or activities) to be performed relative to the fields beinganalyzed in order to maximize yield and/or revenue for the particularfields. In other words, such modules review field condition data andother data to recommend how to effectively enhance output andperformance of the particular fields. The modules may be variouslyreferred to as agricultural intelligence modules or, alternately asrecommendation advisor components or agricultural intelligence services.As used herein, such agricultural intelligence modules may include, butare not limited to a) planting advisor module, b) nitrogen applicationadvisor module, c) pest advisor module, d) field health advisor module,e) harvest advisor module, and f) revenue advisor module.

Agricultural Intelligence Services

Planting Advisor Module

The agricultural intelligence computer system is additionally configuredto provide agricultural intelligence services related to planting. Inone example embodiment, a planting advisor module provides planting daterecommendations. The recommendations are specific to the location of thefield and adapt to the current field condition data, along with weatherpredicted to be experienced by the specific fields.

In one embodiment, the planting advisor module receives one or more ofthe following data points for each field identified by the user (asdetermined from field definition data) in order to determine and providesuch planting date recommendations:

-   -   1. A first set of data points is seed characteristic data. Seed        characteristic data may include any relevant information related        to seeds that are planted or will be planted. Seed        characteristic data may include, for example, seed company data,        seed cost data, seed population data, seed hybrid data, seed        maturity level data, seed disease resistance data, and any other        suitable seed data. Seed company data may refer to the        manufacturer or provider of seeds. Seed cost data may refer to        the price of seeds for a given quantity, weight, or volume of        seeds. Seed population data may include the amount of seeds        planted (or intended to be planted) or the density of seeds        planted (or intended to be planted). Seed hybrid data may        include any information related to the biological makeup of the        seeds (i.e., which plants have been hybridized to form a given        seed.) Seed maturity level data may include, for example, a        relative maturity level of a given seed (e.g., a comparative        relative maturity (“CRM”) value or a silk comparative relative        maturity (“silk CRM”)), growing degree units (“GDUs”) until a        given stage such as silking, mid-pollination, black layer, or        flowering, and a relative maturity level of a given seed at        physiological maturity (“Phy. CRM”). Disease resistance data may        include any information related to the resistance of seeds to        particular diseases. In the example embodiment, disease        resistance data includes data related to the resistance to Gray        Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss's        Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust,        Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex,        Stewart's Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia        Ear Rot, and Fusarium Crown Rot. Other suitable seed data may        include, for example, data related to, grain drydown, stalk        strength, root strength, stress emergence, staygreen, drought        tolerance, ear flex, test eight, plant height, ear height,        mid-season brittle stalk, plant vigor, fungicide response,        growth regulators sensitivity, pigment inhibitors, sensitivity,        sulfonylureas sensitivity, harvest timing, kernel texture,        emergence, harvest appearance, harvest population, seedling        growth, cob color, and husk cover.    -   2. A second set of data points is field-specific data related to        soil composition. Such field-specific data may include        measurements of the acidity or basicity of soil (e.g., pH        levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).    -   3. A third set of data points is field-specific data related to        field data. Such field-specific data may include field names and        identifiers, soil types or classifications, tilling status,        irrigation status.    -   4. A fourth set of data points is field-specific data related to        historical harvest data. Such field-specific data may include        crop type or classification, harvest date, actual production        history (“APH”), yield, grain moisture, and tillage practice.    -   5. In some examples, users may be prompted at the user device to        provide a fifth set of data points by answering questions        regarding desired planting population (e.g., total crop volume        and total crop density for a particular field) and/or seed cost,        expected yield, and indication of risk preference (e.g., general        or specific: user is willing to risk a specific number of        bushels per acre to increase the chance of producing a specific        larger number of bushels per acre) if such information has not        already been provided to the agricultural intelligence computer        system.

The planting advisor module receives and processes the sets of datapoints to simulate possible yield potentials. Possible yield potentialsare calculated for various planting dates. The planting advisor moduleadditionally utilizes additional data to generate such simulations. Theadditional data may include simulated weather between the planting dataand harvesting date, field workability, seasonal freeze risk, droughtrisk, heat risk, excess moisture risk, estimated soil temperature,and/or risk tolerance. The likely harvesting date may be estimated basedupon the provided relative maturity (e.g., to generate an earliestrecommended harvesting date) and may further be adjusted based uponpredicted weather and workability. Risk tolerance may be calculatedbased for a high profit/high risk scenario, a low risk scenario, abalanced risk/profit scenario, and a user defined scenario. The plantingadvisor module generates such simulations for each planting date anddisplays a planting date recommendation for the user on the user device.The recommendation includes the recommended planting date, projectedyield, relative maturity, and graphs the projected yield againstplanting date. In some examples, the planting advisor module also graphsplanting dates against the projected yield loss resulting from springfreeze risk, fall freeze risk, drought risk, heat risk, excess moisturerisk, and estimated soil temperature. In some examples, such graphs aregenerated based on the predicted temperatures and/or precipitationbetween each planting date and a likely or earliest recommended harvestdate for the selected relative maturity. The planting advisor moduleprovides the option of modeling and displaying alternative yieldscenarios for planting data and projected yield by modifying one or moredata points associated with seed characteristic data, field-specificdata, desired planting population and/or seed cost, expected yield,and/or indication of risk preference. The alternative yield scenariosmay be displayed and graphed on the user device along with the originalrecommendation.

In some examples, the planting advisor module recommends or excludesplanting dates based on predicted workability. For example, dates atwhich a predicted planting-specific workability value is “Stop” mayeither be excluded or not recommended. In some examples, the plantingadvisor recommends or excludes planting dates based upon predictedweather events (e.g., temperature or precipitation). For examples,planting dates may be recommended after which likelihood of freezing islower than associated threshold values.

In some examples, the planting advisor recommends seed characteristicsor graphs estimated yield against planting date for various seedcharacteristics. For example, a graph of estimated yield againstplanting date may be generated for both the seed characteristic and arecommended seed characteristic. The recommended seed characteristic maybe recommended based on any of the maximum yield at any planting date,the maximum average yield across a set of planting dates, or theearliest possible harvesting date (e.g., where a later harvesting dateis not desired due to predicted weather, a relative maturity may beselected in order to enable a desired harvesting date).

Nitrogen Application Advisor Module

The agricultural intelligence computer system is additionally configuredto provide agricultural intelligence services related to soil. Thenitrogen application advisor module determines potential needs fornitrogen in the soil and recommends nitrogen application practices to auser. More specifically, the nitrogen application advisor module isconfigured to identify conditions when crop needs cannot be met bynitrogen present in the soil. In one example embodiment, a nitrogenapplication advisor module provides recommendations for sidedressing orspraying, such as date and rate, specific to the location of the fieldand adapted to the current field condition data. In one embodiment, thenitrogen application advisor module is configured to receive one or moreof the following data points for each field identified by the user (asdetermined from field definition data):

-   -   1. A first set of data points includes environmental        information. Environmental information may include information        related to weather, precipitation, meteorology, soil and crop        phenology.    -   2. A second set of data points includes field-specific data        related to field data. Such field-specific data may include        field names and identifiers, soil types or classifications,        tilling status, irrigation status.    -   3. A third set of data points includes field-specific data        related to historical harvest data. Such field-specific data may        include crop type or classification, harvest date, actual        production history (“APH”), yield, grain moisture, and tillage        practice.    -   4. A fourth set of data points is field-specific data related to        soil composition. Such field-specific data may include        measurements of the acidity or basicity of soil (e.g., pH        levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).    -   5. A fifth set of data points is field-specific data related to        planting data. Such field-specific data may include planting        date, seed type or types, relative maturity (RM) levels of        planted seed(s), and seed population. In some examples, the        planting data is transmitted from a planter monitor to the        agricultural intelligence computer system 150, e.g., via a        cellular modem or other data communication device of the planter        monitor.    -   6. A sixth set of data points is field-specific data related to        nitrogen data. Such field-specific data may include nitrogen        application dates, nitrogen application amounts, and nitrogen        application sources.    -   7. A seventh set of data points is field-specific data related        to irrigation data. Such field-specific data may include        irrigation application dates, irrigation amounts, and irrigation        sources.

Based on the sets of data points, the nitrogen application advisormodule determines a nitrogen application recommendation. As describedbelow, the recommendation includes a list of fields with adequatenitrogen, a list of fields with inadequate nitrogen, and a recommendednitrogen application for the fields with inadequate nitrogen.

In some examples, users may be prompted at the user device to answerquestions regarding nitrogen application (e.g., side-dressing, spraying)practices and costs, such as type of nitrogen (e.g., Anhydrous Ammonia,Urea, UAN (Urea Ammonium Nitrate) 28%, 30% or 32%, Ammonium Nitrate,Ammonium Sulphate, Calcium Ammonium Sulphate), nitrogen costs, latestgrowth stage of crop at which nitrogen can be applied, applicationequipment, labor costs, expected crop price, tillage practice (e.g.,type (conventional, no till, reduced, strip) and amount of surface ofthe field that has been tilled), residue (the amount of surface of thefield covered by residue), related farming practices (e.g., manureapplication, nitrogen stabilizers, cover crops) as well as prior cropdata (e.g., crop type, harvest date, Actual Production History (APH),yield, tillage practice), current crop data (e.g., planting date,seed(s) type, relative maturity (RM) of planted seed(s), seedpopulation), soil characteristics (pH, OM, CEC) if such information hasnot already been provided to the agricultural intelligence computersystem. For certain questions, such as latest growth stage of crop atwhich nitrogen can be applied, application equipment, labor costs, theuser has the option to provide a plurality of alternative responses tothat the agricultural intelligence computer system can optimize thenitrogen application advisor recommendation.

Using the environmental information, field-specific data, nitrogenapplication practices and costs, prior crop data, current crop data,and/or soil characteristics, the agricultural intelligence computersystem identifies the available nitrogen in each field and simulatespossible nitrogen application practices, dates, rates, and next date onwhich workability for a nitrogen application is “Green” taking intoaccount predicted workability and nitrogen loss through leaching,denitrification and volatilization. The nitrogen application advisormodule generates and displays on the user device a nitrogen applicationrecommendation for the user. The recommendation includes:

-   -   1. The list of fields having enough nitrogen, including for each        field the available nitrogen, last application data, and the        last nitrogen rate applied.    -   2. The list of fields where nitrogen application is recommended,        including for each field the available nitrogen, recommended        application practice, recommended application dates, recommended        application rate, and next data on which workability for the        nitrogen application is “Green.”

The user has the option of modeling (i.e., running a model) anddisplaying nitrogen lost (total and divided into losses resulting fromvolatilization, denitrification, and leaching) and crop use (“uptake”)of nitrogen over a specified time period (predefined or as defined bythe user) for the recommended nitrogen application versus one or morealternative scenarios based on a custom application practice, date andrate entered by the user. The user has the option of modeling anddisplaying estimated return on investment for the recommended nitrogenapplication versus one or more alternative scenarios based on a customapplication practice, date and rate entered by the user. The alternativenitrogen application scenarios may be displayed and graphed on the userdevice along with the original recommendation. The user has the furtheroption of modeling and displaying estimated yield benefit (minimum,average, and maximum) for the recommended nitrogen application versusone or more alternative scenarios based on a custom applicationpractice, date and rate entered by the user. The user has the furtheroption of modeling and displaying estimated available nitrogen over anytime period specified by the user for the recommended nitrogenapplication versus one or more alternative scenarios based on a customapplication practice, date and rate entered by the user. The user hasthe further option of running the nitrogen application advisor (usingthe nitrogen application advisor) for one or more sub-fields ormanagement zones within a field.

Pest Advisor Module (or Pest and Disease Advisor Module)

The agricultural intelligence computer system is additionally configuredto provide agricultural intelligence services related to pest anddisease. The pest and disease advisor module is configured to identifyrisks posed to crops by pest damage and/or disease damage. In an exampleembodiment, the pest and disease advisor module identifies risks causedby the pests that cause that the most economic damage to crops in theU.S. Such pests include, for example, corn rootworm, corn earworm,soybean aphid, western bean cutworm, European corn borer, armyworm, beanleaf beetle, Japanese beetle, and twospotted spider mite. In someexamples, the pest and disease advisor provides supplemental analysisfor each pest segmented by growth stages (e.g., larval and adultstages). The pest and disease advisor module also identifies diseaserisks caused by the diseases that cause that the most economic damage tocrops in the U.S. Such diseases include, for example, Gray Leaf Spot,Northern Leaf Blight, Anthracnose Stalk Rot, Goss's Wilt, Southern CornLeaf Blight, Eyespot, Common Rust, Anthracnose Leaf Blight, SouthernRust, Southern Virus Complex, Stewart's Leaf Blight, Corn LethalNecrosis, Headsmut, Diplodia Ear Rot, Fusarium Crown Rot. The pestadvisor is also configured to recommend scouting practices and treatmentmethods to respond to such pest and disease risks. The pest advisor isalso configured to provide alerts based on observations of pests inregions proximate to the user's fields.

In one embodiment, the pest and disease advisor may receive one or moreof the following sets of data for each field identified by the user (asdetermined from field definition data):

-   -   1. A first set of data points is environmental information.        Environmental information includes information related to        weather, precipitation, meteorology, crop phenology and pest and        disease reporting.    -   2. A second set of data points is seed characteristic data. Seed        characteristic data may include any relevant information related        to seeds that are planted or will be planted. Seed        characteristic data may include, for example, seed company data,        seed cost data, seed population data, seed hybrid data, seed        maturity level data, seed disease resistance data, and any other        suitable seed data. Seed company data may refer to the        manufacturer or provider of seeds. Seed cost data may refer to        the price of seeds for a given quantity, weight, or volume of        seeds. Seed population data may include the amount of seeds        planted (or intended to be planted) or the density of seeds        planted (or intended to be planted). Seed hybrid data may        include any information related to the biological makeup of the        seeds (i.e., which plants have been hybridized to form a given        seed.) Seed maturity level data may include, for example, a        relative maturity level of a given seed (e.g., a comparative        relative maturity (“CRM”) value or a silk comparative relative        maturity (“silk CRM”)), growing degree units (“GDUs”) until a        given stage such as silking, mid-pollination, black layer, or        flowering, and a relative maturity level of a given seed at        physiological maturity (“Phy. CRM”). Disease resistance data may        include any information related to the resistance of seeds to        particular diseases. In the example embodiment, disease        resistance data includes data related to the resistance to Gray        Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss's        Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust,        Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex,        Stewart's Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia        Ear Rot, and Fusarium Crown Rot. Other suitable seed data may        include, for example, data related to, grain drydown, stalk        strength, root strength, stress emergence, staygreen, drought        tolerance, ear flex, test eight, plant height, ear height,        mid-season brittle stalk, plant vigor, fungicide response,        growth regulators sensitivity, pigment inhibitors, sensitivity,        sulfonylureas sensitivity, harvest timing, kernel texture,        emergence, harvest appearance, harvest population, seedling        growth, cob color, and husk cover.    -   3. A third set of data points is field-specific data related to        planting data. Such field-specific data may include, for        example, planting dates, seed type, relative maturity (RM) of        planted seed, and seed population.    -   4. A fourth set of data points is field-specific data related to        pesticide data. Such field-specific data may include, for        example, pesticide application date, pesticide product type        (specified by, e.g., EPA registration number), pesticide        formulation, pesticide usage rate, pesticide acres tested,        pesticide amount sprayed, and pesticide source.

In some examples, users may be prompted at the user device to answerquestions regarding pesticide application practices and costs, such astype of product type, application date, formulation, rate, acres tested,amount, source, costs, latest growth stage of crop at which pesticidecan be applied, application equipment, labor costs, expected crop priceas well as current crop data (e.g., planting date, seed(s) type,relative maturity (RM) of planted seed(s), seed population) if suchinformation has not already been provided to the agriculturalintelligence computer system. Accordingly, the pest and disease advisormodule receives such data from user devices. For certain questions, suchas latest growth stage of crop at which pesticide can be applied,application equipment, labor costs, the user has the option to provide aplurality of alternative responses to that the agricultural intelligencecomputer system can optimize the pest and disease advisorrecommendation.

The pest and disease advisor module is configured to receive and processall such sets of data points and received user data and simulatepossible pesticide application practices. The simulation of possiblepesticide practices includes, dates, rates, and next date on whichworkability for a pesticide application is “Green” taking into accountpredicted workability. The pest and disease advisor module generates anddisplays on the user device a scouting and treatment recommendation forthe user. The scouting recommendation includes daily (or as specified bythe user) times to scout for specific pests and diseases. The user hasthe option of displaying a specific subset of pests and diseases as wellas additional information regarding a specific pest or disease. Thetreatment recommendation includes the list of fields where a pesticideapplication is recommended, including for each field the recommendedapplication practice, recommended application dates, recommendedapplication rate, and next data on which workability for the pesticideapplication is “Green.” The user has the option of modeling anddisplaying estimated return on investment for the recommended pesticideapplication versus one or more alternative scenarios based on a customapplication practice, date and rate entered by the user. The alternativepesticide application scenarios may be displayed and graphed on the userdevice along with the original recommendation. The user has the furtheroption of modeling and displaying estimated yield benefit (minimum,average, and maximum) for the recommended pesticide application versusone or more alternative scenarios based on a custom applicationpractice, date and rate entered by the user.

Field Health Advisor Module

The field health advisor module identifies crop health quality over thecourse of the season and uses such crop health determinations torecommend scouting or investigation in areas of poor field health. Morespecifically, the field health advisor module receives and processesfield image data to determine, identify, and provide index values ofbiomass health. The index values of biomass health may range from zero(indicating no biomass) to 1 (indicating the maximum amount of biomass).In an example embodiment, the index value has a specific color scheme,so that every image has a color-coded biomass health scheme (e.g., brownareas show the areas in the field with the lowest relative biomasshealth). In one embodiment, the field health advisor module may receiveone or more of the following data points for each field identified bythe user (as determined from field definition data):

-   -   1. A first set of data points includes environmental        information. Such environmental information includes information        related to satellite imagery, aerial imagery, terrestrial        imagery and crop phenology.    -   2. A second set of data points includes field-specific data        related to field data. Such field-specific data may include        field and soil identifiers such as field names, and soil types.    -   3. A third set of data points includes field-specific data        related to soil composition data. Such field-specific data may        include measurements of the acidity or basicity of soil (e.g.,        pH levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).    -   4. A fourth set of data points includes field-specific data        related to planting data. Such field-specific data may include,        for example, planting dates, seed type, relative maturity (RM)        of planted seed, and seed population.

The field health advisor module receives and processes all such datapoints (along with field image data) to determine and identify a crophealth index for each location in each field identified by the user eachtime a new field image is available. In an example embodiment, the fieldhealth advisor module determines a crop health index as a normalizeddifference vegetation index (“NDVI”) based on at least one near-infrared(“NIR”) reflectance value and at least one visible spectrum reflectancevalue at each raster location in the field. In another exampleembodiment, the crop health index is a NDVI based on multispectralreflectance.

The field health advisor module generates and displays on the userdevice the health index map as an overlay on an aerial map for eachfield identified by the user. In an example embodiment, for each field,the field health advisor module will display field image date, growthstage of crop at that time, soil moisture at that time, and health indexmap as an overlay on an aerial map for the field. In an exampleembodiment, the field image resolution is between 5 m and 0.25 cm. Theuser has the option of modeling and displaying a list of fields based onfield image date and/or crop health index (e.g., field with lowestoverall health index values to field with highest overall health indexvalues, field with highest overall health index values to field withlowest overall health index values, lowest health index valuevariability within field, highest health index value variability withinfield, or as specified by the user). The user also has the option ofmodeling and displaying a comparison of crop health index for a fieldover time (e.g., side-by-side comparison, overlay comparison). In anexample embodiment, the field health advisor module provides the userwith the ability to select a location on a field to get more informationabout the health index, soil type or elevation at a particular location.In an example embodiment, the field health advisor module provides theuser with the ability to save a selected location, the relatedinformation, and a short note so that the user can retrieve the sameinformation on the user device while in the field.

A technical effect of the systems and methods described herein includeat least one of (a) improved utilization of agricultural fields throughimproved field condition monitoring; (b) improved selection of time andmethod of fertilization; (c) improved selection of time and method ofpest control; (d) improved selection of seeds planted for the givenlocation of soil; (e) improved field condition data for at a micro-locallevel; and (f) improved selection of time of harvest.

More specifically, the technical effects can be achieved by performingat least one of the following steps: (a) receiving a plurality of fielddefinition data, retrieving a plurality of input data from a pluralityof data networks, determining a field region based on the fielddefinition data, identifying a subset of the plurality of input dataassociated with the field region, determining a plurality of fieldcondition data based on the subset of the plurality of input data, andproviding the plurality of field condition data to the user device; (b)defining a precipitation analysis period, retrieving a set of recentprecipitation data, a set of predicted precipitation data, and a set oftemperature data associated with the precipitation analysis period fromthe subset of the plurality of input data, determining a workabilityindex based on the set of recent precipitation data, the set ofpredicted precipitation data, and the set of temperature data, andproviding a workability value to the user device based on theworkability index; (c) receiving a prospective field activity, anddetermining the workability index based partially on the prospectivefield activity; (d) determining an initial crop moisture level,receiving a plurality of daily high and low temperatures, receiving aplurality of crop water usage, and determining a soil moisture level;(e) receiving a plurality of alert preferences from the user device,identifying a plurality of alert thresholds associated with theplurality of alert preferences, monitoring the subset of the pluralityof input data, and alerting the user device when at least one of thealert thresholds is exceeded; (0 receiving a plurality of fielddefinition data from at least one of a user device and an agriculturalmachine device; (g) identifying a grid associated with the field region,identifying, from a plurality of weather stations associated with thegrid, wherein each of the plurality of weather stations is associatedwith a weather station location, identifying an associated weight foreach of the plurality of weather stations based on each associatedweather station location, receiving a temperature reading from each ofthe plurality of weather stations, and identifying a temperature valuefor the field region based on the plurality of temperature readings andeach associated weight; (h) receiving a plurality of field definitiondata, retrieving a plurality of input data from a plurality of datanetworks, determining a field region based on the field definition data,identifying a subset of the plurality of input data associated with thefield region, determining a plurality of field condition data based onthe subset of the plurality of input data, identifying a plurality offield activity options, determining a recommendation score for each ofthe plurality of field activity options based at least in part on theplurality of field condition data, and providing a recommended fieldactivity option from the plurality of field activity options based onthe plurality of recommendation scores; (i) defining a precipitationanalysis period, retrieving a set of recent precipitation data, a set ofpredicted precipitation data, and a set of temperature data associatedwith the precipitation analysis period from the subset of the pluralityof input data, determining a workability index based on the set ofrecent precipitation data, the set of predicted precipitation data, andthe set of temperature data, and identifying a recommended agriculturalactivity based, at least in part, on the workability index; (j)determining an initial crop moisture level, receiving a plurality ofdaily high and low temperatures, receiving a plurality of crop waterusage, determining a soil moisture level for the field region, andidentifying a plurality of crops to recommend based on the determinedsoil moisture level; (k) determining an expected heat unit value for thefield region based on the input data, receiving a plurality of cropoptions considered for planting, wherein each of the plurality of cropoptions includes crop data, determining a relative maturity for each ofthe plurality of crop options based on the expected heat unit value andthe crop data, and recommending a selected crop from the plurality ofcrop options based on the relative maturity for each of the plurality ofcrop options; (l) receiving a plurality of pest risk data wherein eachof the plurality of pest risk data includes a pest identifier and a pestlocation, receiving a plurality of crop identifiers associated with aplurality of crops, receiving a plurality of pest spray informationassociated with the crop identifiers, determining a pest risk assessmentassociated with each of the plurality of crops, and recommending a spraystrategy based on the plurality of pest risk assessments; (m) receivinga plurality of historical agricultural activities associated with eachof the field region from a user device, and providing a recommendedfield activity option based at least in part on the plurality ofhistorical agricultural activities; and (n) utilizing a grid-based modelto obtain localized field condition data.

As used herein, a processor may include any programmable systemincluding systems using micro-controllers, reduced instruction setcircuits (RISC), application specific integrated circuits (ASICs), logiccircuits, and any other circuit or processor capable of executing thefunctions described herein. The above examples are example only, and arethus not intended to limit in any way the definition and/or meaning ofthe term “processor.”

As used herein, the term “database” may refer to either a body of data,a relational database management system (RDBMS), or to both. As usedherein, a database may include any collection of data includinghierarchical databases, relational databases, flat file databases,object-relational databases, object oriented databases, and any otherstructured collection of records or data that is stored in a computersystem. The above examples are example only, and thus are not intendedto limit in any way the definition and/or meaning of the term database.Examples of RDBMS's include, but are not limited to including, Oracle®Database, MySQL, IBM® DB2, Microsoft® SQL Server, Sybase®, andPostgreSQL. However, any database may be used that enables the systemsand methods described herein. (Oracle is a registered trademark ofOracle Corporation, Redwood Shores, Calif.; IBM is a registeredtrademark of International Business Machines Corporation, Armonk, N.Y.;Microsoft is a registered trademark of Microsoft Corporation, Redmond,Wash.; and Sybase is a registered trademark of Sybase, Dublin, Calif.)

In one embodiment, a computer program is provided, and the program isembodied on a computer readable medium. In an example embodiment, thesystem is executed on a single computer system, without requiring aconnection to a sever computer. In a further embodiment, the system isbeing run in a Windows® environment (Windows is a registered trademarkof Microsoft Corporation, Redmond, Wash.). In yet another embodiment,the system is run on a mainframe environment and a UNIX® serverenvironment (UNIX is a registered trademark of X/Open Company Limitedlocated in Reading, Berkshire, United Kingdom). The application isflexible and designed to run in various different environments withoutcompromising any major functionality. In some embodiments, the systemincludes multiple components distributed among a plurality of computingdevices. One or more components may be in the form ofcomputer-executable instructions embodied in a computer-readable medium.

As used herein, an element or step recited in the singular and proceededwith the word “a” or “an” should be understood as not excluding pluralelements or steps, unless such exclusion is explicitly recited.Furthermore, references to “example embodiment” or “one embodiment” ofthe present disclosure are not intended to be interpreted as excludingthe existence of additional embodiments that also incorporate therecited features.

As used herein, the terms “software” and “firmware” are interchangeable,and include any computer program stored in memory for execution by aprocessor, including RAM memory, ROM memory, EPROM memory, EEPROMmemory, and non-volatile RAM (NVRAM) memory. The above memory types areexample only, and are thus not limiting as to the types of memory usablefor storage of a computer program.

The systems and processes are not limited to the specific embodimentsdescribed herein. In addition, components of each system and eachprocess can be practiced independent and separate from other componentsand processes described herein. Each component and process also can beused in combination with other assembly packages and processes.

The following detailed description illustrates embodiments of thedisclosure by way of example and not by way of limitation. It iscontemplated that the disclosure has general application to themanagement and recommendation of agricultural activities.

FIG. 1 is a diagram depicting an example agricultural environment 100including a plurality of fields that are monitored and managed using anagricultural intelligence computer system. Example agriculturalenvironment 100 includes grower 110 cultivating a plurality of fields120 including a first field 122 and a second field 124. Grower 110interacts with agricultural intelligence computer system 150 toeffectively manage fields 120 and receive recommendations foragricultural activities to effectively utilize fields 120. Agriculturalintelligence computer system 150 utilizes a plurality of computersystems 112, 114, 116, 118, 130A, 130B, and 140 to provide suchservices. Computer systems 112, 114, 116, 118, 130A, 130B, 140, and 150and all associated sub-systems may be referred to as a “networkedagricultural intelligence system.” Although only one grower 110 and onlytwo fields 120 are shown, it should be understood that multiple growers110 having multiple fields 120 may utilize agricultural intelligencecomputer system 150.

In the example embodiment, grower 110 utilizes user devices 112, 114,116, and/or 118 to interact with agricultural intelligence computersystem 150. In one example, user device 112 is a smart watch,computer-enabled glasses, smart phone, PDA, or “phablet” computingdevice capable of transmitting and receiving information such asdescribed herein. Alternately, grower 110 may utilize tablet computingdevice 114, or laptop 116 to interact with agricultural intelligencecomputer system 150. As user devices 112 and 114 are “mobile devices”with specific types and ranges of inputs and outputs, in at least someexamples user devices 112 and 114 utilize specialty software (sometimesreferred to as “apps”) to interact with agricultural intelligencecomputer system 150.

In an example embodiment, agricultural machine 117 (e.g., combine,tractor, cultivator, plow, subsoiler, sprayer or other machinery used ona farm to help with farming) may be coupled to a computing device 118(“agricultural machine computing device”) that interacts withagricultural intelligence computer system 150 in a similar manner asuser devices 112, 114, and 116. In some examples, agricultural machinecomputing device 118 could be a planter monitor, planter controller or ayield monitor. In some examples, the agricultural machine computingdevice 118 could be a planter monitor as disclosed in U.S. Pat. No.8,738,243, incorporated herein by reference, or in International PatentApplication No. PCT/US2013/054506, incorporated herein by reference. Insome examples, the agricultural machine computing device 118 could be ayield monitor as disclosed in U.S. patent application Ser. No.14/237,844, incorporated herein by reference. Agricultural machine 117and agricultural machine computing device 118 may provide agriculturalintelligence computer system 150 with field definition data 160 andfield-specific data, as described below.

As described below and herein, grower (or user) 110 interacts with userdevices 112, 114, 116, and/or 118 to obtain information regarding themanagement of fields 120. More specifically, grower 110 interacts withuser devices 112, 114, 116, and/or 118 in order to obtainrecommendations, services, and information related to the management offields 120. Grower 110 provides field definition data 160 descriptive ofthe location, layout, geography, and topography of fields 120 via userdevices 112, 114, 116, and/or 118. In an example embodiment, grower 110may provide field definition data 160 to agricultural intelligencecomputer system 150 by accessing a map (served by agriculturalintelligence computer system 150) on user device 112, 114, 116, and/or118 and selecting specific CLUs that have been graphically shown on themap. In an alternative embodiment, grower 110 may identify fielddefinition data 160 by accessing a map (served by agriculturalintelligence computer system 150) on user device 112, 114, 116, and/or118 and drawing boundaries of fields 120 (or, more specifically, field122 and field 124) over the map. Such CLU selection or map drawingsrepresent geographic identifiers. In alternative embodiments, the usermay identify field definition data 160 by accessing field definitiondata 160 (provided as shape files or in a similar format) from the U.S.Department of Agriculture Farm Service Agency or other source via theuser device and providing such field definition data 160 to theagricultural intelligence computer system. The land identified by “fielddefinition data” may be referred to as a “field” or “land tract.” Asused herein, the land farmed, or “land tract”, is contained in a regionthat may be referred to as a “field region.” Such a “field region” maybe coextensive with, for example, temperature grids or precipitationgrids, as used and defined below.

Specifically, field definition data 160 defines the location of fields122 and 124. As described herein, accurate locations of fields 122 and124 are useful in order to identify field-specific & environmental data170 and/or field condition data 180. Significant variations may exist infield conditions over small distances including variances in, forexample, soil quality, soil composition, soil moisture levels, nitrogenlevels, relative maturity of crops, precipitation, wind, temperature,solar exposure, other meteorological conditions, and workability of thefield. As such, agricultural intelligence computer system 150 identifiesa location for each of fields 122 and 124 based on field definition data160 and identifies a field region for each of fields 122 and 124. Asdescribed above, in one embodiment agricultural intelligence computersystem 150 utilizes a “grid” architectural model that subdivides landinto grid sections that are 2.5 miles by 2.5 miles in dimension.

Accordingly, agricultural intelligence computer system 150 utilizesfield definition data 160 to identify which field conditions and fielddata to process and determine for a particular field. In the example,data networks 130A and 130B represent data sources associated withfields 124 and 122, respectively, because the grid associated with field122 is monitored by external data source 130B and the grid associatedwith field 124 is monitored by data network 130A. Each of data networks130A and 130B may each have associated subsystems 131A, 132A, 133A, 134A(associated with data network 130A) and 131B, 132B, 133B, and 134B(associated with external data source 130B). Accordingly, fielddefinition data 160 associates field 122 with data network 130A andfield 124 with data network 130B. Such a distinction of regions coveredby an data network 130A and 130B is provided for illustrative purposes.In operation, data networks 130A and 130B may be associated with aplurality of grids and be able to provide field-specific & environmentaldata 170 for a particular grid based on field definition data 160.

Data networks 130A and 130B, as described herein, receive a plurality ofinformation to determine field-specific & environmental data 170. Datanetworks 130A and 130B may receive feeds of meteorological data fromother external services or be associated with meteorological devicessuch as anemometer 135 and rain gauge 136. Accordingly, based on suchdevices 135 and 136 and other accessible data, data networks 130A and130B provide field-specific & environmental data 170 to agriculturalintelligence computer system 150.

Further, agricultural intelligence computer system may receiveadditional information from other data networks 140 to determinefield-specific & environmental data 170 and field condition data 180. Inthe example, other data networks 140 receive inputs from aerialmonitoring system 145 and satellite device 146. Such inputs 145 and 146may provide field-specific & environmental data for a plurality offields 120.

Using field-specific & environmental data 170 associated with each field122 and 124 (as defined by field definition data 160), agriculturalintelligence computer system determines field condition data 180 and/orat least one recommended agricultural activity 190, as described herein.Field condition data 180 substantially represents a response to arequest from grower 110 for information related to field conditions offields 120 including field weather conditions, field workabilityconditions, growth stage conditions, soil moisture, and precipitationconditions. Recommended agricultural activity 190 includes outputs fromany of the plurality of services described herein including plantingadvisor, a nitrogen application advisor, a pest advisor, a field healthadvisor, a harvest advisor, and a revenue advisor. Accordingly,recommended agricultural activity 190 may include, for example,suggestions on planting, nitrogen application, pest response, fieldhealth remediation, harvesting, and sales and marketing of crops.

Agricultural intelligence computer system 150 may be implemented using avariety of distinct computing devices such as agricultural intelligencecomputing devices 151, 152, 153, and 154 using any suitable network. Inan example embodiment, agricultural intelligence computer system 150uses a client-server architecture configured for exchanging data over anetwork (e.g., the Internet) with other computer systems includingsystems 112, 114, 116, 118, 130A, 130B, and 140. One or more userdevices 112, 114, 116, and/or 118 may communicate via a network using asuitable method of interaction including a user application (orapplication platform) stored on user devices 112, 114, 116, and/or 118or using a separate application utilizing (or calling) an applicationplatform interface. Other example embodiments may include other networkarchitectures, such as peer-to-peer or distributed network environment.

The user application may provide server-side functionality, via thenetwork to one or more user devices 112, 114, 116, and/or 118. In anexample embodiment, user device 112, 114, 116, and/or 118 may access theuser application via a web client or a programmatic client. User devices112, 114, 116, and/or 118 may transmit data to, and receive data from,from one or more front-end servers. In an example embodiment, the datamay take the form of requests and user information input, such asfield-specific data, into the user device. One or more front-end serversmay process the user device requests and user information and determinewhether the requests are service requests or content requests, amongother things. Content requests may be transmitted to one or more contentmanagement servers for processing. Application requests may betransmitted to one or more application servers. In an exampleembodiment, application requests may take the form of a request toprovide field condition data and/or agricultural intelligence servicesfor one or more fields 120.

In an example embodiment, agricultural intelligence computer system 150may comprise one or more servers 151, 152, 153, and 154 in communicationwith each other. For example, agricultural intelligence computer system150 may comprise front-end servers 151, application servers 152, contentmanagement servers 153, account servers 154, modeling servers 155,environmental data servers 156, and corresponding databases 157. Asnoted above, environmental data may be obtained from data networks 130A,130B, and 140, accessible by agricultural intelligence computer system150 or such environmental data may be obtained from internal datasources or databases integrated within agricultural intelligencecomputer system 150.

In an example embodiment, data networks 130A, 130B, and 140 may comprisethird-party hosted servers that provide services to agriculturalintelligence computer system 150 via Application Program Interface (API)requests and responses. The frequency at which agricultural intelligencecomputer system 150 may consume data published or made available bythese third-party hosted servers 130A, 130B, and 140 may vary based onthe type of data. In an example embodiment, a notification may be sentto the agricultural intelligence computer system when new data isavailable by a data source. Agricultural intelligence computer system150 may transmit an API call via the network to servers 130A, 130B, and140 hosting the data and receive the new data in response to the call.To the extent needed, agricultural intelligence computer system 150 mayprocess the data to enable components of the agricultural intelligencecomputer system and user application to handle the data. For example,processing data may involve extracting data from a stream or a data feedand mapping the data to a data structure, such as an XML data structure.Data received and/or processed by agricultural intelligence computersystem 150 may be transmitted to the application platform and stored inan appropriate database.

When an application request is made, one or more front end servers 151communicate with applications servers 151, content management servers153, account servers 154, modeling servers 155, environmental dataservers 156, and corresponding databases 157. In one example, modelingservers 155 may generate a predetermined number of simulations (e.g.,10,000 simulations) using, in part, field-specific data andenvironmental data for one or more fields identified based on fielddefinition data and user information. Depending on the type ofapplication request, the field-specific data and environmental data forone or more fields may be located in content management servers 153,account servers 154, environmental data servers 156, correspondingdatabases 157, and, in some instances, archived in modeling servers 155and/or application servers 152. Based on the simulations generated bymodeling servers 155, field condition data and/or agriculturalintelligence services for one or more fields is provided to applicationservers 152 for transmission to the requesting user device 112, 114,116, and/or 118 via the network. More specifically, grower (or user) 110may use user device 112, 114, 116, and/or 118 to access a plurality ofwindows or displays showing field condition data and/or agriculturalintelligence services, as described below.

FIG. 2 is a block diagram of a user computing device 202, used formanaging and recommending agricultural activities, as shown in theagricultural environment of FIG. 1. User computing device 202 mayinclude, but is not limited to, smartphone 112, tablet 114, laptop 116,and agricultural computing device 118 (all shown in FIG. 1).Alternately, user computing device 202 may be any suitable device usedby user 110. In the example embodiment, user system 202 includes aprocessor 205 for executing instructions. In some embodiments,executable instructions are stored in a memory area 210. Processor 205may include one or more processing units, for example, a multi-coreconfiguration. Memory area 210 is any device allowing information suchas executable instructions and/or written works to be stored andretrieved. Memory area 210 may include one or more computer readablemedia.

User system 202 also includes at least one media output component 215for presenting information to user 201. Media output component 215 isany component capable of conveying information to user 201. In someembodiments, media output component 215 includes an output adapter suchas a video adapter and/or an audio adapter. An output adapter isoperatively coupled to processor 205 and operatively couplable to anoutput device such as a display device, a liquid crystal display (LCD),organic light emitting diode (OLED) display, or “electronic ink”display, or an audio output device, a speaker or headphones.

In some embodiments, user system 202 includes an input device 220 forreceiving input from user 201. Input device 220 may include, forexample, a keyboard, a pointing device, a mouse, a stylus, a touchsensitive panel, a touch pad, a touch screen, a gyroscope, anaccelerometer, a position detector, or an audio input device. A singlecomponent such as a touch screen may function as both an output deviceof media output component 215 and input device 220. User system 202 mayalso include a communication interface 225, which is communicativelycouplable to a remote device such as agricultural intelligence computersystem 150. Communication interface 225 may include, for example, awired or wireless network adapter or a wireless data transceiver for usewith a mobile phone network, Global System for Mobile communications(GSM), 3G, or other mobile data network or Worldwide Interoperabilityfor Microwave Access (WIMAX).

Stored in memory area 210 are, for example, computer readableinstructions for providing a user interface to user 201 via media outputcomponent 215 and, optionally, receiving and processing input from inputdevice 220. A user interface may include, among other possibilities, aweb browser and client application. Web browsers enable users, such asuser 201, to display and interact with media and other informationtypically embedded on a web page or a website from agriculturalintelligence computer system 150. A client application allows user 201to interact with a server application from agricultural intelligencecomputer system 150.

As described herein, user system 202 may be associated with a variety ofdevice characteristics. For example device characteristics may vary interms of the operating system used by user device 202 in the initiatingof the first transaction, the browser operating system used by userdevice 202 in the initiating of the first transaction, a plurality ofhardware characteristics associated with user device 202 in theinitiating of the first transaction, the internet protocol addressassociated with user device 202 in the initiating of the firsttransaction, the internet service provider associated with user device202 in the initiating of the first transaction, display attributes andcharacteristics used by a browser used by user device 202 in theinitiating of the first transaction, configuration attributes used by abrowser used by user device 202 in the initiating of the firsttransaction, and software components used by user device 202 in theinitiating of the first transaction. As further described herein,agricultural intelligence computer system 150 (shown in FIG. 1) iscapable of receiving device characteristic data related to user system202 and analyzing such data as described herein.

FIG. 3 is a block diagram of a computing device, used for managing andrecommending agricultural activities, as shown in the agriculturalenvironment of FIG. 1. Server system 301 may include, but is not limitedto, data network systems 130A, 130B, and 140 and agriculturalintelligence computer system 150. In the example embodiment, serversystem 301 determines and analyzes characteristics of devices used inpayment transactions, as described below.

Server system 301 includes a processor 305 for executing instructions.Instructions may be stored in a memory area 310, for example. Processor305 may include one or more processing units (e.g., in a multi-coreconfiguration) for executing instructions. The instructions may beexecuted within a variety of different operating systems on the serversystem 301, such as UNIX, LINUX, Microsoft Windows®, etc. It should alsobe appreciated that upon initiation of a computer-based method, variousinstructions may be executed during initialization. Some operations maybe required in order to perform one or more processes described herein,while other operations may be more general and/or specific to aparticular programming language (e.g., C, C#, C++, Java, Python, orother suitable programming languages, etc.).

Processor 305 is operatively coupled to a communication interface 315such that server system 301 is capable of communicating with a remotedevice such as a user system or another server system 301. For example,communication interface 315 may receive requests from user systems 112,114, 116, and 118 via the Internet, as illustrated in FIGS. 2 and 3.

Processor 305 may also be operatively coupled to a storage device 330.Storage device 330 is any computer-operated hardware suitable forstoring and/or retrieving data. In some embodiments, storage device 330is integrated in server system 301. For example, server system 301 mayinclude one or more hard disk drives as storage device 330. In otherembodiments, storage device 330 is external to server system 301 and maybe accessed by a plurality of server systems 301. For example, storagedevice 330 may include multiple storage units such as hard disks orsolid state disks in a redundant array of inexpensive disks (RAID)configuration. Storage device 330 may include a storage area network(SAN) and/or a network attached storage (NAS) system.

In some embodiments, processor 305 is operatively coupled to storagedevice 330 via a storage interface 320. Storage interface 320 is anycomponent capable of providing processor 305 with access to storagedevice 330. Storage interface 320 may include, for example, an AdvancedTechnology Attachment (ATA) adapter, a Serial ATA (SATA) adapter, aSmall Computer System Interface (SCSI) adapter, a RAID controller, a SANadapter, a network adapter, and/or any component providing processor 305with access to storage device 330.

Memory area 310 may include, but are not limited to, random accessmemory (RAM) such as dynamic RAM (DRAM) or static RAM (SRAM), read-onlymemory (ROM), erasable programmable read-only memory (EPROM),electrically erasable programmable read-only memory (EEPROM), andnon-volatile RAM (NVRAM). The above memory types are exemplary only, andare thus not limiting as to the types of memory usable for storage of acomputer program.

FIG. 4 is an example data flowchart of managing and recommendingagricultural activities using computing devices of FIGS. 1, 2, and 3 inthe agricultural environment shown in FIG. 1. As described herein,grower 110 uses any suitable user device 112, 114, 116, and/or 118(shown in FIG. 1) to specify grower request 401 which is transmitted toagricultural intelligence computer system 150. As described, grower 110uses user application or application platform, served on user device114, to interact with agricultural intelligence computer system 150 andmake any suitable grower request 401. As described herein, growerrequest 401 may include a request for field condition data 180 and/or arequest for a recommended agricultural activity 190.

The application platform (or user application) may provide server-sidefunctionality, via the network to one or more user devices 114. In anexample embodiment, user device 114 may access the application platformvia a web client or a programmatic client. User device 114 may transmitdata to, and receive data, from one or more front-end servers such asfront end server 151 (shown in FIG. 1). In an example embodiment, thedata may take the form of grower requests 401 and user information input402, such as field-specific & environmental data 170 (provided by grower110), into user device 114. One or more front-end servers 151 mayprocess grower requests 401 and user information input 402 and determinewhether grower requests 401 are service requests (i.e., requests forrecommended agricultural activities 190) or content requests (i.e.,requests for field condition data 180), among other things. Contentrequests may be transmitted to one or more content management servers153 (shown in FIG. 1) for processing. Application requests may betransmitted to one or more application servers 152 (shown in FIG. 1). Inan example embodiment, application requests may take the form of agrower request 401 to provide field condition data 180 and/oragricultural intelligence services for one or more fields 120 (shown inFIG. 1).

In an example embodiment, the application platform may comprise one ormore servers 151, 152, 153, and 154 (shown in FIG. 1) in communicationwith each other. For example, agricultural intelligence computer system150 may comprise front-end servers 151, application servers 152, contentmanagement servers 153, account servers 154, modeling servers 155,environmental data servers 156, and corresponding databases 157 (allshown in FIG. 1). Further, agricultural intelligence computer systemincludes a plurality of agricultural intelligence modules 158 and 159.In the example embodiment, agricultural intelligence modules 158 and 159are harvest advisor module 158 and revenue advisor module 159. Infurther examples, planting advisor module, nitrogen application advisormodule, pest and disease advisor module, and field health advisor modulemay be represented in agricultural intelligence computer system 150. Asnoted above, environmental data may be obtained from data networks 130and 140 accessible by agricultural intelligence computer system 150 orit may be obtained from internal data sources integrated withinagricultural intelligence computer system 150.

In an example embodiment, data networks 130 and 140 may comprisethird-party hosted servers that provide services to agriculturalintelligence computer system 150 via Application Program Interface (API)requests and responses. The frequency at which agricultural intelligencecomputer system 150 may consume data published or made available bythese third-party hosted servers 130 and 140 may vary based on the typeof data. In an example embodiment, a notification may be sent toagricultural intelligence computer system 150 when new data is madeavailable. Agricultural intelligence computer system 150 may alternatelytransmit an API call via the network to external data sources 130hosting the data and receive the new data in response to the call. Tothe extent needed, agricultural intelligence computer system 150 mayprocess the data to enable components of the application platform tohandle the data. For example, processing data may involve extractingdata from a stream or a data feed and mapping the data to a datastructure, such as an XML data structure. Data received and/or processedby agricultural intelligence computer system 150 may be transmitted tothe application platform and stored in an appropriate database.

When an application request is made, one or more application servers 152communicate with content management servers 153, account servers 154,modeling servers 155, environmental data servers 156, and correspondingdatabases 157. In one example, modeling servers 155 may generate apredetermined number of simulations (e.g., 10,000 simulations) using, inpart, field-specific & environmental data 170 for one or more fields 120identified based on field definition data 160 and user input information402. Depending on the type of grower request 401, field-specific &environmental data 170 for one or more fields 120 may be located incontent management servers 153, account servers 154, modeling servers155, environmental data servers 156, and corresponding databases 157,and, in some instances, archived in the application servers 152. Basedon the simulations generated by modeling servers 155, field conditiondata 180 and/or agricultural intelligence services (i.e., recommendedagricultural activities 190) for one or more fields 120 is provided toapplication servers 152 for transmission to requesting user device 114via the network. More specifically, the user may use user device 114 toaccess a plurality of windows or displays showing field condition data180 and/or recommended agricultural activities 190, as described below.

Although the aforementioned application platform has been configuredwith various exemplary embodiments above, one skilled in the art willappreciate that any configuration of servers may be possible and thatexample embodiments of the present disclosure need not be limited to theconfigurations disclosed herein.

In order to provide field condition data 180, agricultural intelligencecomputer system 150 runs a plurality of field condition data analysismodules 410. Field condition analysis modules include field weather datamodule 411 which is configured to determine weather conditions for eachfield 120 identified by grower 110. Agricultural intelligence computersystem 150 uses field weather data module 411 to determine fieldtemperature, wind, humidity, and dew point. Agricultural intelligencecomputer system 150 also uses field weather data module 411 to determineforecasted weather conditions including field temperature, wind,humidity, and dew point for hourly projected intervals, daily projectedintervals, or any interval specified by grower 110. Field precipitationmodule 415, field workability module 412, and field growth stage module413 also receive and process the forecasted weather conditions.Near-term forecasts are determined using a meteorological model (e.g.,the Microcast model) while long-term projections are determined usinghistorical analog simulations.

Agricultural intelligence computer system 150 uses grid temperatures todetermine temperature values. Known research shows that using gridtechniques provides more accurate temperature measurements thanpoint-based temperature reporting. Temperature grids are typicallysquare physical regions, typically 2.5 miles by 2.5 miles. Agriculturalintelligence computer system 150 associates fields (e.g., fields 122 or124) with a temperature grid that contains the field. Agriculturalintelligence computer system 150 identifies a plurality of weatherstations that are proximate to the temperature grid. Agriculturalintelligence computer system 150 receives temperature data from theplurality of weather stations. The temperatures reported by theplurality of weather stations are weighted based on their relativeproximity to the grid such that more proximate weather stations havehigher weights than less proximate weather stations. Further, therelative elevation of the temperature grid is compared to the elevationof the plurality of weather stations. Temperature values reported by theplurality of weather stations are adjusted in response to the relativedifference in elevation. In some examples, the temperature grid includesor is adjacent to a body of water. Bodies of water are known to cause areduction in the temperature of an area. Accordingly, when a particularfield is proximate to a body of water as compared to the weather stationproviding the temperature reading, the reported temperature for thefield is adjusted downwards to account for the closer proximity to thebody of water.

Precipitation values are similarly determined using precipitation gridsthat utilize meteorological radar data. Precipitation grids have similarpurposes and characteristics as temperature grids. Specifically,agricultural intelligence computer system 150 uses available datasources such as the National Weather Service's NEXRAD Doppler radardata. Agricultural intelligence computer system 150 further validatesand calibrates reported data with ground station and satellite data. Inthe example embodiment, the Doppler radar data is obtained for theprecipitation grid. The Doppler radar data is used to determine anestimate of precipitation for the precipitation grid. The estimatedprecipitation is adjusted based on other data sources such as otherweather radar sources, ground weather stations (e.g., rain gauges),satellite precipitation sources (e.g., the National Oceanic andAtmospheric Administration's Satellite Applications and Research), andmeteorological sources. By utilizing multiple distinct data sources,more accurate precipitation tracking may be accomplished.

Current weather conditions and forecasted weather conditions (hourly,daily, or as specified by the user) are displayed on the user devicegraphically along with applicable information regarding the specificfield, such as field name, crop, acreage, field precipitation, fieldworkability, field growth stage, soil moisture, and any other fielddefinition data or field-specific & environmental data 170 that the usermay specify. Such information may be displayed on the user device in oneor more combinations and level of detail as specified by the user.

In an example embodiment, temperature can be displayed as hightemperatures, average temperatures and low temperatures over time.Temperature can be shown during a specific time and/or date range and/orharvest year and compared against prior times, years, including a 5 yearaverage, a 15 year average, a 30 year average or as specified by theuser.

In an example embodiment, field precipitation module 415 determines andprovides the amount of precipitation and/or accumulated precipitationover time. Precipitation can be shown during a specific time periodand/or date range and/or harvest year and compared against prior times,years, including a 5 year average, a 15 year average, a 30 year averageor as specified by the user. Precipitation can also be displayed as pastand future radar data. In an example embodiment, past radar may bedisplayed over the last 1.5 hours or as specified by the user. Futureradar may be displayed over the next 6 hours or as specified by theuser. Radar may be displayed as an overlay of an aerial image mapshowing the user's one or more fields where the user has the ability tozoom in and out of the map. Radar can be displayed as static atintervals selected by the user or continuously over intervals selectedby the user. The underlying radar data received and/or processed by theagricultural intelligence computer system may be in the form of GriddedBinary (GRIB) files that includes forecast reflectivity files,precipitation type, and precipitation-typed reflectivity values.

As part of field condition data 180 provided, agricultural intelligencecomputer system 150 runs or executes field workability data module 412,which processes field-specific & environmental data 170 and userinformation output 402 to determine the degree to which a field orsection of a field (associated with the field definition data) may beworked for a given time of year using machinery or other implements. Inan example embodiment, agricultural intelligence computer system 150retrieves field historical precipitation data over a predeterminedperiod of time, field predicted precipitation over a predeterminedperiod of time, and field temperatures over a predetermined period oftime. The retrieved data is used to determine one or more workabilityindex as determined by field workability data module 412.

In an example embodiment, the workability index may be used to derivethree values of workability for particular farm activities. The value of“Good” workability indicates high likelihood that field conditions areacceptable for use of machinery or a specified activity during anupcoming time interval. The value of “Check” workability indicates thatfield conditions may not be ideal for the use of machinery or aspecified activity during an upcoming time interval. The value of “Stop”workability indicates that field conditions are not suitable for work ora specified activity during an upcoming time interval.

Determined values of workability may vary depending upon the farmactivity. For example, planting and tilling typically require a lowlevel of muddiness and may require a higher workability index to achievea value of “Good” than activities that allow for a higher level ofmuddiness. In some embodiments, workability indices are distinctlycalculated for each activity based on a distinct set of factors. Forexample, a workability index for planting may correlate to predictedtemperature over the next 60 hours while a workability index forharvesting may be correlated to precipitation alone. In some examples,user may be prompted at the user device to answer questions regardingfield activities if such information has not already been provided toagricultural intelligence computer system 150. For example, a user maybe asked what field activities are currently in use. Depending upon theresponse, agricultural intelligence computer system 150 may adjust itscalculations of the workability index because of the user's activities,thereby incorporating the feedback of the user into the calculation ofthe workability index. Alternately, agricultural intelligence computersystem 150 may adjust the recommendations made to the user foractivities. In a further example, agricultural intelligence computersystem 150 may recommend that the user stop such activities based on theresponses.

As part of field condition data 180 provided, agricultural intelligencecomputer system 150 runs or executes field growth stage data module 413(e.g., for corn, vegetative (VE-VT) and reproductive (R1-R6) growthstages). Field growth stage data module 413 receives and processesfield-specific & environmental data 170 and user information input 402to determine timings of key farming decisions. Agricultural intelligencecomputer system 150 computes crop progression for each crop throughstages of growth (agronomic stages) by tracking the impact of weather onthe phenomenological development of the crop from planting throughharvest.

In the example embodiment, agricultural intelligence computer system 150uses the planting date entered by the user device. Alternately,agricultural intelligence computer system 150 may estimate the plantingdate using a system algorithm. Specifically, the planting date may beestimated based on agronomic stage data and planting practices in theregion associated with the field definition data. The planting practicesmay be received from a data service such as a university data networkthat monitors typical planting techniques for a region. Agriculturalintelligence computer system 150 further uses data regarding the user'sfarming practices within the current season and for historical seasons,thereby facilitating historical analysis. Agricultural intelligencecomputer system 150 determines a relative maturity value of the cropsbased on expected heat units over the growing season in light of theplanting date, the user's farming practices, and field-specific &environmental data 170. As heat is a proxy for energy received by crops,agricultural intelligence computer system 150 calculates expected heatunits for crops and determines a development of maturity of the crops.

As part of field condition data 180 provided, agricultural intelligencecomputer system 150 uses and executes soil moisture data module 414.Soil moisture data module 414 is configured to determine the percent oftotal water capacity available to the crop that is present in the soilof the field. Soil moisture data module 414 initializes output at thebeginning of the growing season based on environmental data inagricultural intelligence computer system 150 at that time, such as datafrom the North American Land Data Assimilation System, andfield-specific & environmental data 170.

Soil moisture values are then adjusted, at least daily, during thegrowing season by tracking moisture entering the soil via precipitationand moisture leaving the soil via evapotranspiration (ET). Precipitationexcludes a calculated amount of water that never enters the soil becauseit is lost as runoff. A runoff value is determined based on theprecipitation amount over time and a curve determined by the USDAclassification of soil type. The agricultural intelligence computersystems accounts for a user's specific field-specific & environmentaldata 170 related to soil to determine runoff and the runoff curve forthe specific field. Lighter, sandier soils allow greater precipitationwater infiltration and experience less runoff during heavy precipitationevents than heavier, more compact soils. Heavier or denser soil typeshave lower precipitation infiltration rates and lose more precipitationto runoff on days with large precipitation events.

Daily evapotranspiration associated with a user's specific field iscalculated based on a version of the standard Penman-Monteith ET model.The total amount of water that is calculated as leaving the soil throughevapotranspiration on a given day is based on the following:

-   -   1. Maximum and minimum temperatures for the day: Warmer        temperatures result in greater evapotranspiration values than        cooler temperatures.    -   2. Latitude: During much of the corn growing season, fields at        more northern latitudes experience greater solar radiation than        fields at more southern latitudes due to longer days. But fields        at more northern latitudes also get reduced radiation due to        earth tilting. Areas with greater net solar radiation values        will have relatively higher evapotranspiration values than areas        with lower net solar radiation values.    -   3. Estimated crop growth stage: Growth stages around pollination        provide the highest potential daily evapotranspiration values        while growth stages around planting and late in grain fill        result in relatively lower daily evapotranspiration values,        because the crop uses less water in these stages of growth.    -   4. Current soil moisture: The agricultural intelligence computer        system's model accounts for the fact that crops conserve and use        less water when less water is available in the soil. The        reported soil moisture values reported that are above a certain        percentage, determined by crop type, provide the highest        potential evapotranspiration values and potential        evapotranspiration values decrease as soil moisture values        approach 0%. As soil moisture values fall below this percentage,        corn will start conserving water and using soil moisture at less        than optimal rates. This water conservation by the plant        increases as soil moisture values decrease, leading to lower and        lower daily evapotranspiration values.    -   5. Wind: Evapotranspiration takes into account wind; however,        evapotranspiration is not as sensitive to wind as to the other        conditions. In an example embodiment, a set wind speed of 2        meters per second is used for all evapotranspiration        calculations.

Agricultural intelligence computer system 150 is additionally configuredto provide alerts based on weather and field-related information.Specifically, the user may define a plurality of thresholds for each ofa plurality of alert categories. When field condition data indicatesthat the thresholds have been exceeded, the user device will receivealerts. Alerts may be provided via the application (e.g., notificationupon login, push notification), email, text messages, or any othersuitable method. Alerts may be defined for crop cultivation monitoring,for example, hail size, rainfall, overall precipitation, soil moisture,crop scouting, wind conditions, field image, pest reports or diseasereports. Alternately, alerts may be provided for crop growth strategy.For example, alerts may be provided based on commodity prices, grainprices, workability indexes, growth stages, and crop moisture content.In some examples, an alert may indicate a recommended course of action.For example, the alert may recommend that field activities (e.g.,planting, nitrogen application, pest and disease treatment, irrigationapplication, scouting, or harvesting) occur within a particular periodof time. Agricultural intelligence computer system 150 is alsoconfigured to receive information on farming activities from, forexample, the user device, an agricultural machine, or any other source.Accordingly, alerts may also be provided based on logged farm activitysuch as planting, nitrogen application, spraying, irrigation, scouting,or harvesting. In some examples, alerts may be provided regardless ofthresholds to indicate certain field conditions. In one example, a dailyprecipitation, growth stage, field image or temperature alert may beprovided to the user device.

Agricultural intelligence computer system 150 is further configured togenerate a plurality of reports based on field condition data 180. Suchreports may be used by the user to improve strategy and decision-makingin farming. The reports may include reports on crop growth stage,temperature, humidity, soil moisture, precipitation, workability, andpest risk. The reports may also include one or more field definitiondata 160, field-specific & environmental data 170, scouting and loggingevents, field condition data 180, summary of agricultural intelligenceservices (e.g., recommended agricultural activities 190) or FSA Form578.

Agricultural intelligence computer system 150 is also configured toreceive supplemental information from the user device. For example, auser may provide logging or scouting events regarding the fieldsassociated with the field definition data. The user may access a loggingapplication at the user device and update agricultural intelligencecomputer system 150. In one embodiment, the user accesses agriculturalintelligence computer system 150 via a user device while beingphysically located in a field to enter field-specific data. Theagricultural intelligence computer system might automatically displayand transmit the date and time and field definition data associated withthe field-specific data, such as geographic coordinates and boundaries.The user may provide general data for activities including field,location, date, time, crop, images, and notes. The user may also providedata specific to particular activities such as planting, nitrogenapplication, pesticide application, harvesting, scouting, and currentweather observations. Such supplemental information may be associatedwith the other data networks and used by the user for analysis.

Agricultural intelligence computer system 150 is additionally configuredto display scouting and logging events related to the receipt offield-specific data from the user via one or more agricultural machinesor agricultural machine devices that interacts with agriculturalintelligence computer system 150 or via the user device. Suchinformation can be displayed as specified by the user. In one example,the information is displayed on a calendar on the user device, whereinthe user can obtain further details regarding the information asnecessary. In another example, the information is displayed in a tableon the user device, wherein the user can select the specific categoriesof information that the user would like displayed.

Agricultural Intelligence Modules 420

Planting Advisor Module 421

Agricultural intelligence computer system 150 is additionally configuredto provide agricultural intelligence services related to planting. Morespecifically, agricultural intelligence computer system 150 includes aplurality of agricultural intelligence modules 420 (or agriculturalactivity modules) that may be used to determine recommended agriculturalactivities 190 which are provided to grower 110. In at least someexamples, agricultural intelligence modules 420 may be similar toagricultural intelligence modules 158 and 159 (shown in FIG. 1). In atleast some examples, planting advisor module 421 may be similar toagricultural intelligence modules 158 and 159 (shown in FIG. 1). Suchagricultural intelligence modules 420 may be referred to as agriculturalintelligence services and may include planting advisor module 421,nitrogen application advisor module 422, pest advisor module 423, fieldhealth advisor module 424, and harvest advisor module 425. In oneexample embodiment, planting advisor module 421 processes field-specific& environmental data 170 and user information input 402 to determine andprovide planting date recommendations. The recommendations are specificto the location of the field and adapt to the current field conditiondata.

In one embodiment, planting advisor module 421 receives one or more ofthe following data points for each field identified by the user (asdetermined from field definition data) in order to determine and providesuch planting date recommendations:

-   -   1. A first set of data points is seed characteristic data. Seed        characteristic data may include any relevant information related        to seeds that are planted or will be planted. Seed        characteristic data may include, for example, seed company data,        seed cost data, seed population data, seed hybrid data, seed        maturity level data, seed disease resistance data, and any other        suitable seed data. Seed company data may refer to the        manufacturer or provider of seeds. Seed cost data may refer to        the price of seeds for a given quantity, weight, or volume of        seeds. Seed population data may include the amount of seeds        planted (or intended to be planted) or the density of seeds        planted (or intended to be planted). Seed hybrid data may        include any information related to the biological makeup of the        seeds (i.e., which plants have been hybridized to form a given        seed.) Seed maturity level data may include, for example, a        relative maturity level of a given seed (e.g., a comparative        relative maturity (“CRM”) value or a silk comparative relative        maturity (“silk CRM”)), growing degree units (“GDUs”) until a        given stage such as silking, mid-pollination, black layer, or        flowering, and a relative maturity level of a given seed at        physiological maturity (“Phy. CRM”). Disease resistance data may        include any information related to the resistance of seeds to        particular diseases. In the example embodiment, disease        resistance data includes data related to the resistance to Gray        Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss's        Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust,        Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex,        Stewart's Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia        Ear Rot, and Fusarium Crown Rot. Other suitable seed data may        include, for example, data related to, grain drydown, stalk        strength, root strength, stress emergence, staygreen, drought        tolerance, ear flex, test eight, plant height, ear height,        mid-season brittle stalk, plant vigor, fungicide response,        growth regulators sensitivity, pigment inhibitors, sensitivity,        sulfonylureas sensitivity, harvest timing, kernel texture,        emergence, harvest appearance, harvest population, seedling        growth, cob color, and husk cover.    -   2. A second set of data points is field-specific data related to        soil composition. Such field-specific data may include        measurements of the acidity or basicity of soil (e.g., pH        levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).    -   3. A third set of data points is field-specific data related to        field data. Such field-specific data may include field names and        identifiers, soil types or classifications, tilling status,        irrigation status.    -   4. A fourth set of data points is field-specific data related to        historical harvest data. Such field-specific data may include        crop type or classification, harvest date, actual production        history (“APH”), yield, grain moisture, and tillage practice.    -   5. In some examples, users may be prompted at the user device to        provide a fifth set of data points by answering questions        regarding desired planting population (e.g., total crop volume        and total crop density for a particular field) and/or seed cost,        expected yield, and indication of risk preference (e.g., general        or specific: user is willing to risk a specific number of        bushels per acre to increase the chance of producing a specific        larger number of bushels per acre) if such information has not        already been provided to the agricultural intelligence computer        system.

Planting advisor module 421 receives and processes the sets of datapoints to simulate possible yield potentials. Possible yield potentialsare calculated for various planting dates. Planting advisor module 421additionally utilizes additional data to generate such simulations. Theadditional data may include simulated weather between the planting dataand harvesting date, field workability, seasonal freeze risk, droughtrisk, heat risk, excess moisture risk, estimated soil temperature,and/or risk tolerance. Risk tolerance may be calculated based for a highprofit/high risk scenario, a low risk scenario, a balanced risk/profitscenario, and a user defined scenario. Planting advisor module 421generates such simulations for each planting date and displays aplanting date recommendation for the user on the user device. Therecommendation includes the recommended planting date, projected yield,relative maturity, and graphs the projected yield against planting date.In some examples, the planting advisor module also graphs the projectedyield against the planting date for spring freeze risk, the plantingdate for fall freeze risk, the planting date for drought risk, theplanting date for heat risk, the planting date for excess moisture risk,the planting date for estimated soil temperature, and the planting datefor the various risk tolerance levels. Planting advisor module 421provides the option of modeling and displaying alternative yieldscenarios for planting data and projected yield by modifying one or moredata points associated with seed characteristic data, field-specificdata, desired planting population and/or seed cost, expected yield,and/or indication of risk preference. The alternative yield scenariosmay be displayed and graphed on the user device along with the originalrecommendation.

Nitrogen Application Advisor Module 422

Agricultural intelligence computer system 150 is additionally configuredto provide agricultural intelligence services related to soil by usingnitrogen application advisor module 422. In at least some examples,nitrogen application advisor module 422 may be similar to agriculturalintelligence modules 158 and 159 (shown in FIG. 1). Nitrogen applicationadvisor module 422 determines potential needs for nitrogen in the soiland recommends nitrogen application practices to a user. Morespecifically, nitrogen application advisor module 422 is configured toidentify conditions when crop needs cannot be met by nitrogen present inthe soil. In one example embodiment, nitrogen application advisor module422 provides recommendations for sidedressing or spraying, such as dateand rate, specific to the location of the field and adapt to the currentfield condition data. In one embodiment, nitrogen application advisormodule 422 is configured to receive one or more of the following datapoints for each field identified by the user (as determined from fielddefinition data):

-   -   1. A first set of data points includes environmental        information. Environmental information may include information        related to weather, precipitation, meteorology, soil and crop        phenology.    -   2. A second set of data points includes field-specific data        related to field data. Such field-specific data may include        field names and identifiers, soil types or classifications,        tilling status, irrigation status.    -   3. A third set of data points includes field-specific data        related to historical harvest data. Such field-specific data may        include crop type or classification, harvest date, actual        production history (“APH”), yield, grain moisture, and tillage        practice.    -   4. A fourth set of data points is field-specific data related to        soil composition. Such field-specific data may include        measurements of the acidity or basicity of soil (e.g., pH        levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).    -   5. A fifth set of data points is field-specific data related to        planting data. Such field-specific data may include planting        date, seed type or types, relative maturity (RM) levels of        planted seed(s), and seed population.    -   6. A sixth set of data points is field-specific data related to        nitrogen data. Such field-specific data may include nitrogen        application dates, nitrogen application amounts, and nitrogen        application sources.    -   7. A seventh set of data points is field-specific data related        to irrigation data. Such field-specific data may include        irrigation application dates, irrigation amounts, and irrigation        sources.

Based on the sets of data points, nitrogen application advisor module422 determines a nitrogen application recommendation. As describedbelow, the recommendation includes a list of fields with adequatenitrogen, a list of fields with inadequate nitrogen, and a recommendednitrogen application for the fields with inadequate nitrogen.

In some examples, users may be prompted at the user device to answerquestions regarding nitrogen application (e.g., side-dressing, spraying)practices and costs, such as type of nitrogen (e.g., Anhydrous Ammonia,Urea, UAN (Urea Ammonium Nitrate) 28%, 30% or 32%, Ammonium Nitrate,Ammonium Sulphate, Calcium Ammonium Sulphate), nitrogen costs, latestgrowth stage of crop at which nitrogen can be applied, applicationequipment, labor costs, expected crop price, tillage practice (e.g.,type (conventional, no till, reduced, strip) and amount of surface ofthe field that has been tilled), residue (the amount of surface of thefield covered by residue), related farming practices (e.g., manureapplication, nitrogen stabilizers, cover crops) as well as prior cropdata (e.g., crop type, harvest date, Actual Production History (APH),yield, tillage practice), current crop data (e.g., planting date,seed(s) type, relative maturity (RM) of planted seed(s), seedpopulation), soil characteristics (pH, OM, CEC) if such information hasnot already been provided to the agricultural intelligence computersystem. For certain questions, such as latest growth stage of crop atwhich nitrogen can be applied, application equipment, labor costs, theuser has the option to provide a plurality of alternative responses tothat the agricultural intelligence computer system can optimize thenitrogen application advisor recommendation.

Using the environmental information, field-specific data, nitrogenapplication practices and costs, prior crop data, current crop data,and/or soil characteristics, nitrogen application advisor module 422identifies the available nitrogen in each field and simulates possiblenitrogen application practices, dates, rates, and next date on whichworkability for a nitrogen application is “Green” taking into accountpredicted workability and nitrogen loss through leaching,denitrification and volatilization. Nitrogen application advisor module422 generates and displays on the user device a nitrogen applicationrecommendation for the user. The recommendation includes:

-   -   1. The list of fields having enough nitrogen, including for each        field the available nitrogen, last application data, and the        last nitrogen rate applied.    -   2. The list of fields where nitrogen application is recommended,        including for each field the available nitrogen, recommended        application practice, recommended application dates, recommended        application rate, and next data on which workability for the        nitrogen application is “Green.”    -   3. The recommended date of nitrogen application for each field.        In some examples the recommended date may be optimized for        either yield or return on investment. In some examples the        recommended date may be the date at which minimum predicted        nitrogen levels in the field will reach a threshold minimum        value without intervening nitrogen application. In some examples        recommended dates may be excluded or selected based upon        available equipment as indicated by the user; for example, where        no equipment for applying nitrogen is available past a given        growth stage, dates are preferably recommended before the        predicted date at which that growth stage will be reached.    -   4. The recommended rate of nitrogen application for each field        for each possible or recommended application date. The        recommended rate of nitrogen application may be optimized for        either yield or return on investment.

The user has the option of modeling and displaying nitrogen lost (totaland divided into losses resulting from volatilization, denitrification,and leaching) and crop use (“uptake”) of nitrogen over a specified timeperiod (predefined or as defined by the user) for the recommendednitrogen application versus one or more alternative scenarios based on acustom application practice, date and rate entered by the user. The userhas the option of modeling and displaying estimated return on investmentfor the recommended nitrogen application versus one or more alternativescenarios based on a custom application practice, date and rate enteredby the user. The alternative nitrogen application scenarios may bedisplayed and graphed on the user device along with the originalrecommendation. The user has the further option of modeling anddisplaying estimated yield benefit (minimum, average, and maximum) forthe recommended nitrogen application versus one or more alternativescenarios based on a custom application practice, date and rate enteredby the user. The user has the further option of modeling and displayingestimated available nitrogen over any time period specified by the userfor the recommended nitrogen application versus one or more alternativescenarios based on a custom application practice, date and rate enteredby the user. The user has the further option of running the nitrogenapplication advisor (using the nitrogen application advisor) for one ormore sub-fields or management zones within a field.

Pest Advisor Module (or Pest and Disease Advisor Module) 423

Agricultural intelligence computer system 150 is additionally configuredto provide agricultural intelligence services related to pest anddisease by using pest advisor module 423. In at least some examples,pest advisor module 423 may be similar to agricultural intelligencemodules 158 and 159 (shown in FIG. 1). Pest advisor module 423 isconfigured to identify risks posed to crops by pest damage and/ordisease damage. In an example embodiment, pest advisor module 423identifies risks caused by the pests that cause that the most economicdamage to crops in the U.S. Such pests include, for example, cornrootworm, corn earworm, soybean aphid, western bean cutworm, Europeancorn borer, armyworm, bean leaf beetle, Japanese beetle, and twospottedspider mite. In some examples, the pest and disease advisor providessupplemental analysis for each pest segmented by growth stages (e.g.,larval and adult stages). Pest advisor module 423 also identifiesdisease risks caused by the diseases that cause that the most economicdamage to crops in the U.S. Such diseases include, for example, GrayLeaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss's Wilt,Southern Corn Leaf Blight, Eyespot, Common Rust, Anthracnose LeafBlight, Southern Rust, Southern Virus Complex, Stewart's Leaf Blight,Corn Lethal Necrosis, Headsmut, Diplodia Ear Rot, Fusarium Crown Rot.The pest advisor is also configured to recommend scouting practices andtreatment methods to respond to such pest and disease risks. Pestadvisor module 423 is also configured to provide alerts based onobservations of pests in regions proximate to the user's fields.

In one embodiment, pest advisor module 423 may receive one or more ofthe following sets of data for each field identified by the user (asdetermined from field definition data):

-   -   1. A first set of data points is environmental information.        Environmental information includes information related to        weather, precipitation, meteorology, crop phenology and pest and        disease reporting. In some examples, pest and disease reports        may be received from a third-party server or data source such as        a university or governmental reporting service.    -   2. A second set of data points is seed characteristic data. Seed        characteristic data may include any relevant information related        to seeds that are planted or will be planted. Seed        characteristic data may include, for example, seed company data,        seed cost data, seed population data, seed hybrid data, seed        maturity level data, seed disease resistance data, and any other        suitable seed data. Seed company data may refer to the        manufacturer or provider of seeds. Seed cost data may refer to        the price of seeds for a given quantity, weight, or volume of        seeds. Seed population data may include the amount of seeds        planted (or intended to be planted) or the density of seeds        planted (or intended to be planted). Seed hybrid data may        include any information related to the biological makeup of the        seeds (i.e., which plants have been hybridized to form a given        seed.) Seed maturity level data may include, for example, a        relative maturity level of a given seed (e.g., a comparative        relative maturity (“CRM”) value or a silk comparative relative        maturity (“silk CRM”)), growing degree units (“GDUs”) until a        given stage such as silking, mid-pollination, black layer, or        flowering, and a relative maturity level of a given seed at        physiological maturity (“Phy. CRM”). Disease resistance data may        include any information related to the resistance of seeds to        particular diseases. In the example embodiment, disease        resistance data includes data related to the resistance to Gray        Leaf Spot, Northern Leaf Blight, Anthracnose Stalk Rot, Goss's        Wilt, Southern Corn Leaf Blight, Eyespot, Common Rust,        Anthracnose Leaf Blight, Southern Rust, Southern Virus Complex,        Stewart's Leaf Blight, Corn Lethal Necrosis, Headsmut, Diplodia        Ear Rot, and Fusarium Crown Rot. Other suitable seed data may        include, for example, data related to, grain drydown, stalk        strength, root strength, stress emergence, staygreen, drought        tolerance, ear flex, test eight, plant height, ear height,        mid-season brittle stalk, plant vigor, fungicide response,        growth regulators sensitivity, pigment inhibitors, sensitivity,        sulfonylureas sensitivity, harvest timing, kernel texture,        emergence, harvest appearance, harvest population, seedling        growth, cob color, and husk cover.    -   3. A third set of data points is field-specific data related to        planting data. Such field-specific data may include, for        example, planting dates, seed type, relative maturity (RM) of        planted seed, and seed population.    -   4. A fourth set of data points is field-specific data related to        pesticide data. Such field-specific data may include, for        example, pesticide application date, pesticide product type        (specified by, e.g., EPA registration number), pesticide        formulation, pesticide usage rate, pesticide acres tested,        pesticide amount sprayed, and pesticide source.

In some examples, users may be prompted at the user device to answerquestions regarding pesticide application practices and costs, such astype of product type, application date, formulation, rate, acres tested,amount, source, costs, latest growth stage of crop at which pesticidecan be applied, application equipment, labor costs, expected crop priceas well as current crop data (e.g., planting date, seed(s) type,relative maturity (RM) of planted seed(s), seed population) if suchinformation has not already been provided to the agriculturalintelligence computer system. Accordingly, pest advisor module 423receives such data from user devices. For certain questions, such aslatest growth stage of crop at which pesticide can be applied,application equipment, labor costs, the user has the option to provide aplurality of alternative responses to that agricultural intelligencecomputer system 150 can optimize the pest and disease advisorrecommendation.

Pest advisor module 423 is configured to receive and process all suchsets of data points and received user data and simulate possiblepesticide application practices. The simulation of possible pesticidepractices includes, dates, rates, and next date on which workability fora pesticide application is “Green” taking into account predictedworkability. Pest advisor module 423 generates and displays on the userdevice a scouting and treatment recommendation for the user. Thescouting recommendation includes daily (or as specified by the user)times to scout for specific pests and diseases. The user has the optionof displaying a specific subset of pests and diseases as well asadditional information regarding a specific pest or disease. Thetreatment recommendation includes the list of fields where a pesticideapplication is recommended, including for each field the recommendedapplication practice, recommended application dates, recommendedapplication rate, and next data on which workability for the pesticideapplication is “Green.” The user has the option of modeling anddisplaying estimated return on investment for the recommended pesticideapplication versus one or more alternative scenarios based on a customapplication practice, date and rate entered by the user. The alternativepesticide application scenarios may be displayed and graphed on the userdevice along with the original recommendation. The user has the furtheroption of modeling and displaying estimated yield benefit (minimum,average, and maximum) for the recommended pesticide application versusone or more alternative scenarios based on a custom applicationpractice, date and rate entered by the user.

Field Health Advisor Module 424

Agricultural intelligence computer system 150 is also configured toprovide information regarding the health and quality of areas of fields120. In at least some examples, field health advisor module 424 may besimilar to agricultural intelligence modules 158 and 159 (shown in FIG.1). Field health advisor module 424 identifies crop health quality overthe course of the season and uses such crop health determinations torecommend scouting or investigation in areas of poor field health. Morespecifically, field health advisor module 424 receives and processesfield image data to determine, identify, and provide index values ofbiomass health. The index values of biomass health may range from zero(indicating no biomass) to 1 (indicating the maximum amount of biomass).In an example embodiment, the index value has a specific color scheme,so that every image has a color-coded biomass health scheme (e.g., brownareas show the areas in the field with the lowest relative biomasshealth). In one embodiment, field health advisor module 424 may receiveone or more of the following data points for each field identified bythe user (as determined from field definition data):

-   -   1. A first set of data points includes environmental        information. Such environmental information includes information        related to satellite imagery, aerial imagery, terrestrial        imagery and crop phenology.    -   2. A second set of data points includes field-specific data        related to field data. Such field-specific data may include        field and soil identifiers such as field names, and soil types.    -   3. A third set of data points includes field-specific data        related to soil composition data. Such field-specific data may        include measurements of the acidity or basicity of soil (e.g.,        pH levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).    -   4. A fourth set of data points includes field-specific data        related to planting data. Such field-specific data may include,        for example, planting dates, seed type, relative maturity (RM)        of planted seed, and seed population.

Field health advisor module 424 receives and processes all such datapoints (along with field image data) to determine and identify a crophealth index for each location in each field identified by the user eachtime a new field image is available. In an example embodiment, fieldhealth advisor module 424 determines a crop health index as a normalizeddifference vegetation index (“NDVI”) based on at least one near-infrared(“NIR”) reflectance value and at least one visible spectrum reflectancevalue at each raster location in the field. In another exampleembodiment, the crop health index is a NDVI based on multispectralreflectance.

Field health advisor module 424 generates and displays on the userdevice the health index map as an overlay on an aerial map for eachfield identified by the user. In an example embodiment, for each field,the field health advisor module will display field image date, growthstage of crop at that time, soil moisture at that time, and health indexmap as an overlay on an aerial map for the field. In an exampleembodiment, the field image resolution is between 5 m and 0.25 cm. Theuser has the option of modeling and displaying a list of fields based onfield image date and/or crop health index (e.g., field with lowestoverall health index values to field with highest overall health indexvalues, field with highest overall health index values to field withlowest overall health index values, lowest health index valuevariability within field, highest health index value variability withinfield, or as specified by the user). The user also has the option ofmodeling and displaying a comparison of crop health index for a fieldover time (e.g., side-by-side comparison, overlay comparison). In anexample embodiment, the field health advisor module provides the userwith the ability to select a location on a field to get more informationabout the health index, soil type or elevation at a particular location.In an example embodiment, the field health advisor module provides theuser with the ability to save a selected location, the relatedinformation, and a short note so that the user can retrieve the sameinformation on the user device while in the field.

Harvest Advisor Module 425

Agricultural intelligence computer system 150 is additionally configuredto provide agricultural intelligence services related to timing andmechanisms of harvest using harvest advisor module 425. In at least someexamples, harvest advisor module 425 may be similar to agriculturalintelligence modules 158 and 159 (shown in FIG. 1) and more specificallyto harvest advisor module 158.

Harvest advisor computing module 425 is in data communication withagricultural intelligence computing system 150. Agriculturalintelligence computing system 150 captures and stores field definitiondata 160, field-specific & environmental data 170, and field conditiondata 180 within its memory device. Harvest advisor computing module 425receives and processes field definition data 160, field-specific &environmental data 170, and field condition data 180 from agriculturalintelligence computing system 150 to provide (i) grain moisture valuepredictions during drydown of a particular field prior to harvest, (ii)a projected date when the particular field will reach a target moisturevalue, and (iii) harvest recommendations and planning for one or morefields. More specifically, harvest advisor computing module 425 isconfigured to: (i) identify an initial date of a crop within a field(e.g., a black layer date); (ii) identifying an initial moisture valueassociated with the crop and the initial date; (iii) identify a targetharvest moisture value associated with the crop; (iv) receive fieldcondition data associated with the field; (v) compute a target harvestdate for the crop based at least in part on the initial date, theinitial moisture value, the field condition data, and the target harvestmoisture value, wherein the target harvest date indicates a date atwhich the crop will have a present moisture value approximately equal tothe target harvest moisture value; and (vi) display the target harvestdate for the crop to the grower for harvest planning. The target harvestmoisture value represents the value at which grower 110 desires the cropto be when harvested (e.g., at harvest date). Thus, the harvest advisorcomputing module 425 assists the grower in projecting approximately whena given field will be ready for harvest by projecting moisture valuesover time, and considering both past weather data and future weatherpredictions at the given field.

Revenue Advisor Module 426

Agricultural intelligence computer system 150 is additionally configuredto provide agricultural intelligence services related to selling andmarketing crops using revenue advisor module 426. In at least someexamples, revenue advisor module 426 may be similar to agriculturalintelligence modules 158 and 159 (shown in FIG. 1) and more specificallyto revenue advisor module 159.

Revenue advisor module 426 is in data communication with agriculturalintelligence computing system 150. Agricultural intelligence computingsystem 150 captures and stores field definition data 160, field-specific& environmental data 170, and field condition data 180 within its memorydevice. Revenue advisor module 426 receives and processes fielddefinition data 160 and field condition data 180 from agriculturalintelligence computing system 150 to provide (i) daily yield projectionsat the national, farm, and field level, (ii) current crop prices at thenational and local level, (iii) daily revenue projections at the farmand field level, and (iv) daily profit estimates by the field, farm, andacre. More specifically, revenue advisor module 426 is configured to:(i) receive field condition data 180 and field definition data 160 fromagricultural intelligence computing system 150 for each field 120 ofgrower 110, wherein the field condition data 180 includes growth stageconditions, field weather conditions, soil moisture, and precipitationconditions, and wherein field definition data includes fieldidentifiers, geographic identifiers, boundary identifiers, and cropidentifiers; (ii) receive cost data from grower 110, wherein cost dataincludes costs related to an individual field 120 or all of the fieldsassociated with grower 110; (iii) receive crop pricing data from localand national sources; (iv) process field condition data 180, the croppricing data, and the cost data to determine yield data, revenue data,and profit data for each field 120 of grower 110; and (v) output theyield data, revenue data and profit data to user device 112, 114, 116,and/or 118. The yield data, revenue data, and profit data relate to anindividual field, and can further relate a plurality of additionalfields associated with the grower. Yield data includes yield estimatesfor a high, low, and expected case for each field and at the nationallevel. Revenue data includes revenue estimates based on national andlocal prices for each field. Profit data includes the expected profitfor each field for the high, low, and expected cases.

FIG. 5 is an example method for managing agricultural activities inagricultural environment 100 (shown in FIG. 1). Method 500 isimplemented by agricultural intelligence computer system 150 (shown inFIG. 1). Agricultural intelligence computer system 150 receives 510 aplurality of field definition data. Agricultural intelligence computersystem 150 retrieves 520 a plurality of input data from a plurality ofdata networks 130A, 130B, and 140. Agricultural intelligence computersystem 150 determines 530 a field region based on the field definitiondata. Agricultural intelligence computer system 150 identifies 540 asubset of the plurality of input data associated with the field region.Agricultural intelligence computer system 150 determines 550 a pluralityof field condition data based on the subset of the plurality of inputdata. Agricultural intelligence computer system 150 provides 560 theplurality of field condition data to the user device.

FIG. 6 is an example method for recommending agricultural activities inthe agricultural environment of FIG. 1. Method 500 is implemented byagricultural intelligence computer system 150 (shown in FIG. 1).Agricultural intelligence computer system 150 receives 610 a pluralityof field definition data. Agricultural intelligence computer system 150retrieves 620 a plurality of input data from a plurality of datanetworks 130A, 130B, and 140. Agricultural intelligence computer system150 determines 630 a field region based on the field definition data.Agricultural intelligence computer system 150 identifies 640 a subset ofthe plurality of input data associated with the field region.Agricultural intelligence computer system 150 determines 650 a pluralityof field condition data based on the subset of the plurality of inputdata. Agricultural intelligence computer system 150 provides 660 theplurality of field condition data to the user device. Agriculturalintelligence computer system 150 determines 670 a recommendation scorefor each of the plurality of field activity options based at least inpart on the plurality of field condition data. Agricultural intelligencecomputer system 150 provides 680 a recommended field activity optionfrom the plurality of field activity options based on the plurality ofrecommendation scores.

FIG. 7 is a diagram of components of one or more example computingdevices that may be used in the environment shown in FIG. 5. FIG. 7further shows a configuration of databases including at least database157 (shown in FIG. 1). Database 157 is coupled to several separatecomponents within fraud detection computer system 150, which performspecific tasks.

Agricultural intelligence computer system 150 includes a first receivingcomponent 701 for receiving a plurality of field definition data, afirst retrieving component 702 for retrieving a plurality of input datafrom a plurality of data networks, a first determining component 703 fordetermining a field region based on the field definition data, a firstidentifying component 704 for identifying a subset of the plurality ofinput data associated with the field region, a second determiningcomponent 705 for determining a plurality of field condition data basedon the subset of the plurality of input data, a first providingcomponent 706 for providing the plurality of field condition data to theuser device, a third determining component 707 for determining arecommendation score for each of the plurality of field activity optionsbased at least in part on the plurality of field condition data, and asecond providing component 708 for providing a recommended fieldactivity option from the plurality of field activity options based onthe plurality of recommendation scores.

In an example embodiment, database 157 is divided into a plurality ofsections, including but not limited to, a meteorological analysissection 710, a soil and crop analysis section 712, and a market analysissection 714. These sections within database 157 are interconnected toupdate and retrieve the information as required

FIGS. 8-30 are example illustrations of information provided by theagricultural intelligence computer system of FIG. 3 to the user deviceof FIG. 2 to facilitate the management and recommendation ofagricultural activities.

Referring to FIG. 8, screenshot 800 illustrates a setup screen whereingrower 110 (shown in FIG. 1) may provide user information input 402(shown in FIG. 4) to define basic attributes associated with theiraccount.

Referring to FIGS. 9-11, screenshots 900, 1000, and 1100 illustrateoptions allowing for grower 110 (shown in FIG. 1) to view fieldcondition data 180 (shown in FIG. 1). As is indicated in screenshot 900,grower 110 may select particular dates for field condition data 180viewing that may be in the past, present, or future and may accordinglyprovide historic, current, or forecasted field condition data 180.Grower 110 may accordingly select a particular date and time to viewfield condition data 180 for particular fields 120 (shown in FIG. 1).Screenshot 1000 illustrates a consolidated view of field condition data180 for a particular field 120 at a particular date. More specifically,field condition data 180 shown includes output of field weather datamodule 411, field workability data module 412, growth stage data module413, and soil moisture data module 414. Screenshot 1100 similarly showsoutput of field precipitation module 415 of a particular field 120 overa particular time period. As described above and herein, such fieldcondition data 180 is determined using a localized method thatdetermines such field conditions uniquely for each field 120.

FIGS. 12 and 13 illustrate such field condition data 180 displayedgraphically using maps. More specifically, from the view of screenshots1200, grower 110 may select a particular portion of a map to identifyfield condition data 180 for each of fields 120. Screenshot 1300accordingly illustrates such a display of field condition data 180 for aparticular field 122.

Referring to FIGS. 14-20, screenshots 1400, 1500, 1600, 1700, 1800,1900, and 2000 illustrate the display of fields 120 (shown in FIG. 1)associated with grower 110 (shown in FIG. 1). More specifically, inscreenshot 1400 grower 110 provides field definition data 160 (shown inFIG. 1) to define fields 120, indicated graphically. Accordingly, aplurality of fields 120 are illustrated and may be reviewed individuallyor in any combination to obtain field condition data 180 (shown inFIG. 1) and/or recommended agricultural activities 190 (shown in FIG.1). Note that screenshot 1400 illustrates that grower 110 may own, use,or otherwise manage a plurality of fields 120 that are substantially farfrom one another and associated with unique geographic andmeteorological conditions. It will be appreciated that the systems andmethods described herein, providing hyper localized field condition data180 and recommended agricultural activities 190, substantially helpsgrower 110 to identify meaningful distinctions between each of fields120 in order to effectively manage each field 120.

In screenshot 1500, grower 110 (shown in FIG. 1) may see a tabular viewindicating identifiers for each field 120 (shown in FIG. 1) inconjunction with a map view of such fields. Grower 110 may navigateusing the tabular view (or the graphical view) to individual actionsassociated with each field 120. Accordingly, screenshot 1600 illustratesenhanced information shown to grower 110 upon selecting a particularfield for review from either the tabular view or the graphical view(e.g., by clicking on one of the fields). As is illustrated inscreenshots 1700, 1800, 1900, and 2000, grower 110 may additionallyenhance display (or “zoom in”) to view a smaller subset of fields 120.

Referring to FIGS. 21 and 22, screenshots 2100 and 2200 illustratehistorical data that may be provided by grower 110 (shown in FIG. 1) orany other source to identify notes or details associated with planting.More specifically, grower 110 may navigate to a particular date inscreenshot 2400 and view planting notes as displayed in screenshot 2200.

Referring to FIG. 23, screenshot 2300 presents a tabular view thatallows grower 110 (shown in FIG. 1) to group or consolidate common landunits (“CLUs”) into “field groups”. As a result, data associated with aparticular field group may be viewed commonly. In some examples, grower110 may be interested in viewing and managing particular fields 120(shown in FIG. 1) in particular combinations based on, for example,common crops or geographies. Accordingly, the application and systemsdescribed facilitate such effective management.

Referring to FIGS. 24-30, screenshots 2400, 2500, 2600, 2700, 2800,2900, and 3000 illustrate the use of a “field manager” tool that enablesgrower 110 (shown in FIG. 1) to view information for a plurality offields in a tabular format. Screenshots 2400, 2500, 2600, 2700, 2800,2900, and 3000 further indicate that grower 110 may view field conditiondata 180 in common with field-specific & environmental data 170 (shownin FIG. 1). For example screenshot 2400 illustrates, on a per fieldbasis, current cultivated crop, acreage, average yield, tillingpractices or methods, and residue levels. By contrast, screenshot 2500illustrates that grower 110 may apply a filter 2510 to identifyparticular subgroups of fields 120 for review based on characteristicsincluding current cultivated crop, acreage, average yield, tillingpractices or methods, and residue levels. The field manager tool alsoenables grower 110 to update or edit information. Screenshots 2600,2700, 2800, 2900, and 3000 show views wherein grower 110 may update oredit information for previous periods of cultivation. More specifically,in screenshot 2600, general data may be updated while in screenshot2700, planting data may be updated. Similarly, in screenshot 2800,harvest data may be updated and in screenshot 2900, nitrogen data may beupdated. In screenshot 3000, soil characteristics data may be updated.

FIG. 31 is a screenshot of an example field management interface 3100that may be used for managing crop harvesting activities for one or morefields 120 (shown in FIG. 1) of user 110 (shown in FIG. 1). In theexample embodiment, harvest advisor computing module 158 (shown inFIG. 1) is in data communication with agricultural intelligencecomputing system 150 (shown in FIG. 1), and harvest advisor computingmodule 158 presents user interface 3100 to user 110 for the variousdescribed field management activities. In other embodiments,agricultural intelligence computing system 150 presents user interface3100 to user 110.

In the example embodiment, interface 3100 is a field management screendisplaying a plurality of fields 3110, one field per row 3112 in thedisplay. Generally speaking, user interface 3100 presents an interfacethrough which user 110 can view data associated with each of theirfields 3110 (e.g., aspects of field condition data 180, field definitiondata 160, and field specific data 170, all shown in FIG. 1), as well asinput or change that data. Such data may be stored in, for example,databases 157 (shown in FIG. 1) of agricultural intelligence computingsystem 150.

In the example embodiment, each row 3112 displays data associated with aparticular field (e.g., fields 122, 124 shown in FIG. 1). Morespecifically, each row 3112 includes a field name 3114, an average yield3116, a moisture measurement date 3118, a measured grain moisture value3120, a harvest year 3122, and a black layer date 3124. Some of thefields may not have some associated data stored in agriculturalintelligence computing system 150.

Moisture measurement date 3118 and measured grain moisture value 3120,in the example embodiment, are related values that represent aparticular moisture sample or an aggregate of moisture samples takenfrom the associated field on a particular date. The moisture measurementdate 3118 is a date at which the sample(s) were taken, and the measuredgrain moisture value 3120 is the moisture measurement (e.g., moisturepercentage) of the sample(s) taken on that date.

Harvest year 3122 represents the calendar year in which the dataelements for the associated field 3112 are applicable. In other words,the displayed data for field 3112 is applicable to the growing year of2014.

Black layer date 3124, in the example embodiment, is a date commonlyused by growers of corn to indicate a milestone of crop maturity afterwhich grain moisture normally begins to decline.

In some embodiments, user 110 may input one or more data elements for aparticular field through interface 3100. For example, user 110 mayselect a single field 3112 and click an “edit selected” button 3140,which causes to be displayed an “Edit Harvest Data” section 3130 havingone or more data entry fields. In the example embodiment, “Edit HarvestData” section 3130 includes data entry fields for average yield 3132,moisture measurement date 3134, measured grain moisture 3136, and blacklayer date 3138. When user 110 enters one or more values in section3130, these values are stored as the corresponding data elements averageyield 3116, moisture measurement date 3118, measured grain moisturevalue 3120, and black layer date 3124, and displayed in the associatedrow 3112 for that field.

In some embodiments, user 110 may also enter a target moisture value(not shown in FIG. 31) for one or more fields 3110. Target moisturevalue, in the example embodiment, represents a moisture value (e.g., amoisture percentage) at which the grower (e.g., user 110) wouldgenerally prefer the grain to be at when harvested. For example, absentother factors, user 110 may prefer corn to be at 21% moisture at thetime of harvest. Such preferences may, for example, help user 110 reducedrydown costs associated with the harvested crop if the crop wereharvested at that stage (e.g., once the crop has reached 21% moisture).In the example embodiment, harvest advisor computing module 158 promptsuser 110 for a single target moisture value, and applies this targetmoisture value to all fields 3110 of a given type (e.g., all cornfields). In some embodiments, user 110 may enter individual, separatetarget moisture values for each field 3110. In other words, a targetmoisture value may be independently input and/or edited by user 110 foreach field 3110.

One or more of these data elements may be used by harvest advisorcomputing module 158 and/or user 110 to manage crop harvestingactivities as described below.

FIG. 32 is a screenshot of an example harvest advisor interface 3200presented to user 110 (shown in FIG. 1) by harvest advisor computingmodule 158 to manage crop harvesting activities. In the exampleembodiment, harvest advisor computing module 158 (shown in FIG. 1) is indata communication with agricultural intelligence computing system 150(shown in FIG. 1), and harvest advisor computing module 158 presentsuser interface 3500 to user 110 for the various described fieldmanagement activities.

In the example embodiment, interface 3200 displays data specific to anindividual user, and includes an un-harvested fields section 3210 and analready-harvested fields section 3250 for the fields of that user.Un-harvested fields section 3210 displays data regarding fields not yetindicated as harvested within agricultural intelligence computing system150. More specifically, in the example embodiment, each un-harvestedfield of user 110 is represented as an individual row 3212 inun-harvested fields section 3210. Each row 3212 includes a field name3214, a field planting date 3216, a present grain moisture value 3218, atarget moisture date 3220, an upcoming risk indicator 3222, and adetails button 3224. Field name 3214 may be similar to field name 3114(shown in FIG. 31).

Planting date 3216, in the example embodiment, is as described above inreference to planting advisor module 421, and generally indicates thedate at which seed was planted in the associated field identified byfield name 3214. In the example embodiment, planting date 3216 alsoincludes a relative maturity (RM) value of the crop in the associatedfield, also as described above.

Grain moisture value 3218, in the example embodiment, represents anactual or a projected (e.g., computed) grain moisture value of the cropin the associated field. For example, grain moisture value 3218 mayrepresent a percentage of grain moisture as of the current day. In someembodiments, this value 3218 may be an actual (e.g., sampled) value or acomputed (e.g., estimated) value. For example, an actual value may be anindividual sample moisture value or an aggregate of one or more samplemoisture values taken from the associated field, such as measured grainmoisture 3120, 3136 (shown in FIG. 31). Alternatively, grain moisturevalue 3218 may be a computed value calculated by agriculturalintelligence computing system 150 or harvest advisor computing module158, as described below.

Target moisture date 3220, in the example embodiment, generallyrepresents the date at which the crop in the associated field reachesthe target moisture value (not shown in FIG. 32). Target moisture date3220 may be an actual date (e.g., the known date at which the crop inthe associated field reached the target moisture value) or may be aprojected date (e.g., a computed date calculated by agriculturalintelligence computing system 150 or harvest advisor computing module158, as described below).

Upcoming risks 3222 is a data element that presents an indication offuture risks to harvesting activities. For example, wind and rain maypresent environmental restrictions or difficulties to harvesting ofcrops. Growers such as user 110 may use such data for planning harvestactivities. In the example embodiment, upcoming risks 3222 presents riskdata in the upcoming 3 days. Further, if forecasted wind is greater thana pre-determined threshold (e.g., deemed to create risk for harvest,such as greater than 10 or 15 miles per hour (MPH)), or if thepercentage chance of rain is greater than 50%, then an associated riskwill be displayed as an upcoming risk 3222 for the associated field.Such risk data may be determined from data networks, machines, or users,for example, weather forecast data such as precipitation forecasts andwind forecasts (e.g., from field weather data module 411 and/or fieldprecipitation module 415, both shown and described in respect to FIG.4). In some embodiments, harvest advisor computing module 158 computes arisk value associated with these risks based at least in part on theabove risk data and/or field condition data.

View details 3224, in the example embodiment, is an interface buttonthat presents the user 110 a detailed view of grain moisture for thecrop in the associated field. More specifically, the detailed viewillustrates actual and/or approximate grain moisture values for the cropover time, starting from the black layer date (e.g., black layer date3124 (FIG. 31)) and through at least the target moisture date 3220. Thisdetailed view is described in greater detail below in reference to FIG.33.

In the example embodiment, after harvesting a particular field 3212,that field may be designated as harvested in agricultural intelligencecomputing system 150. Harvest advisor interface 3200 includes a harvestindicator area 3230 having input fields for an actual harvest date 3232(e.g., Sep. 29, 2014), an actual yield 3234 (e.g., 150 bushels peracre), and an actual grain moisture 3236 on the harvest date (e.g.,26%). During operation, in the example embodiment, after a particularfield is harvested, user 110 selects the field in the un-harvestedfields section 3210, enters the actual harvest values in harvestindicator area 3230, and saves the data. After designating theparticular field as harvested, that field is removed from theun-harvested fields section 3210 and is inserted into the harvestedfields section 3250 below. In other embodiments, an agricultural machineand/or agricultural machine computing device may transmit an indicationof field harvest to harvest advisor computing module 158, and/or maytransmit or otherwise supply one or more of actual harvest date 3232,actual yield 3234, and actual grain moisture 3236 to harvest advisorcomputing module 158.

Harvested fields section 3250, in the example embodiment, displays allof the harvested fields for user 110. Harvested fields section 3250includes, for each field, a field name 3214, an actual harvest date3252, an actual grain moisture 3254, an actual drydown cost 3256, and aview details button 3258. In some embodiments, actual harvest date 3252may be the date entered in harvest date 3232, and actual grain moisture3254 may be the value entered in actual grain moisture 3236, asdescribed above in reference to harvest indicator area 3230. In theexample embodiment, actual drydown cost 3256 is an actual costassociated with drying down the harvested crop of the associated fielduntil it reaches a particular moisture value (e.g., the target moisturevalue, or some other moisture value). View details button 3258 for analready-harvested field may additionally display a line representingactual harvest date 3252.

FIG. 33 is a screenshot of an example harvest advisor display 3300 thatmay be used by user 110 (shown in FIG. 1) to manage crop harvestingactivities. In the example embodiment, display 3300 is the subject of aparticular individual field, such as un-harvested field 3212 (shown inFIG. 32), and display 3300 is presented to user 110 after clickingdisplay button 3224 of the subject field on interface 3200 shown on FIG.32. Harvest advisor computing module 158 (shown in FIG. 1) is in datacommunication with agricultural intelligence computing system 150 (shownin FIG. 1), and harvest advisor computing module 158 presents userinterface 3300 to user 110 for the various described field managementactivities.

In the example embodiment, display 3300 includes a target moisture value3310 (“target grain moisture”) for the particular field, as describedabove in reference to FIGS. 31 and 32. In this example, target moisturevalue 3310 for the subject field is set to 16%. Display 3300 alsoincludes a projected target harvest date, or just “target harvest date”3320. Target harvest date 3320, generally speaking, represents the dateat which the crop in the associated field is projected to reach targetmoisture value 3310.

In the example embodiment, display 3300 also includes a grain moisturegraph 3330. Graph 3330 is defined by an X-axis 3334 expressed as a dateand a Y-axis 3332 expressed as a percentage. More specifically, X-axis3334 is a date beginning at the date at which a dry down milestone hasbeen reached (e.g., black layer date 3124 of the subject field) andcontinuing to a date up to and/or past target harvest date 3320. Y-axis3332 is a percentage moisture value ranging from between an initialmoisture value (e.g., a moisture value at the black layer date, such as37%) and a lower threshold such as, for example, a value at or below thetarget moisture value 3310 (e.g., 13%). In some embodiments, graph 3330may include a vertical line marking the present date, thereby givinguser 110 a visual indication of a present projection for crop moisturein the associated field. Further, in some embodiments, graph 3330 mayinclude a display of one or more actual data points (e.g., actual samplevalues taken from the associated field and inputted into agriculturalintelligence computing system 150).

Graph 3330, in the example embodiment, includes a trace 3340 (“timeseries”) of grain moisture as a percentage, and over time. Trace 3340includes a starting point 3332, a target harvest date point 3344 at atarget harvest date 3346, and a target harvest window 3348 as defined bytarget harvest date point 3344 and a harvest window end date 3350.Starting point, in some embodiments, is a data point defined by blacklayer date 3124 and an initial moisture value (e.g., 37%). These valuesmay be values projected by harvest advisor computing module 158 (e.g., aprojected black layer date and associated moisture value on that date),or they may be values input by user 110 (e.g., inputted as a moisturemeasurement date 3134 and measured grain moisture value 3136 entered viainterface 3100 shown in FIG. 31). In some embodiments, trace 3340 maystart before or after black layer date 3124. For example, trace 3340 maystart from another known sample value inputted by user 110.

Target harvest date point 3344, in the example embodiment, includes atarget harvest date 3346, a date at which the moisture value (e.g.,plotted by trace 3340) first reaches the target moisture value 3310.Harvest advisor computing module 158 determines target harvest datepoint 3344 and target harvest date 3346 during or after generation oftrace 3340. Further, harvest advisor computing module 158 alsodetermines a harvest window end date 3350 after which trace 3340 (e.g.,the projected moisture value of the grain) drops below target moisturevalue 3310. The target harvest date 3346 and the harvest window end date3350 define a target harvest window 3348 within which the crop isprojected to be at approximately the target moisture value 3310. In someembodiments, a delta threshold may be provided that defines a rangearound target moisture value. For example, a delta threshold of 1.0% mayindicate that target harvest date 3346 is when the projected moisturevalue is approximately equal to target moisture value 3310, and harvestwindow end date 3350 is when the projected moisture value is 1.0% lessthan target harvest moisture value 3310. And in some embodiments, thisdelta threshold value may be implemented around target harvest moisturevalue 3310. For example, a delta threshold of 1.0% may indicate thattarget harvest date 3346 is when the projected moisture value is 0.5%greater than target harvest moisture value 3310, and harvest window enddate 3350 is when the projected moisture value is 0.5% less than targetharvest moisture value 3310. Further, in some embodiments, user 110 maydefine this delta threshold value, or may define an upper target harvestmoisture value and a lower harvest moisture value that may be usedsimilarly.

In the example embodiment, trace 3340 is generated by harvest advisorcomputing module 158 starting from a beginning point 3342 defined byblack layer date 3124 and the associated initial moisture value at thatblack layer date (e.g., 37%). Generally speaking, corn has an initialmoisture value at the black layer date, from which time the grain ceasesto uptake any further moisture. As such, the moisture value of the grainwill only go down after the black layer date. To project moisturevalues, harvest advisor computing module 158 identifies an initial dateand an associated initial moisture value. In some embodiments, theinitial date is a maturity date of a crop within the subject field, suchas black layer date 3124, and the initial moisture value is a moisturevalue on that date. In other embodiments, the initial date is a date ofa collected sample from the subject field, and the initial moisturevalue is the sampled moisture value or aggregation of sample values.

In the example embodiment, harvest advisor computing module 158 alsoidentifies a target harvest moisture value, such as target harvestmoisture value 3310. Further, harvest advisor computing module 158receives field condition data associated with the subject field.Generally speaking, harvest advisor computing module 158 includes fieldcondition data that may have an appreciable impact on the drying of thegrain, or the value of grain moisture, as time progresses towardharvest. In the example embodiment, harvest advisor computing module 158receives one or more of the following data points for each fieldidentified by the user (as determined from field definition data) inorder to determine and provide such harvest recommendations:

-   -   1. A first set of data points is environmental information.        Environmental information includes information related to        weather, precipitation, meteorology, and crop phenology. Such        information may include, in particular, humidity, temperature,        and wind.    -   2. A second set of data points is seed characteristic data. Seed        characteristic data may include any relevant information related        to seeds that are planted. Seed characteristic data may include,        for example, seed population data, seed hybrid data, seed        maturity level data, and any other suitable seed data. Seed        population data may include the amount of seeds planted or the        density of seeds planted. Seed hybrid data may include any        information related to the biological makeup of the seeds (i.e.,        which plants have been hybridized to form a given seed.) Seed        maturity level data may include, for example, a relative        maturity level of a given seed (e.g., a comparative relative        maturity (“CRM”) value or a silk comparative relative maturity        (“silk CRM”)), growing degree units (“GDUs”) until a given stage        such as silking, mid-pollination, black layer, or flowering, and        a relative maturity level of a given seed at physiological        maturity (“Phy. CRM”). Other suitable seed data may include, for        example, data related to, grain drydown, stalk strength, root        strength, stress emergence, staygreen, drought tolerance, ear        flex, test eight, plant height, ear height, mid-season brittle        stalk, plant vigor, fungicide response, growth regulators        sensitivity, pigment inhibitors, sensitivity, sulfonylureas        sensitivity, harvest timing, kernel texture, emergence, harvest        appearance, harvest population, seedling growth, cob color, and        husk cover.    -   3. A third set of data points is field-specific data related to        planting data. Such field-specific data may include, for        example, planting date, seed type, relative maturity (RM) of        planted seed, and seed population.    -   4. A fourth set of data points includes field-specific data        related to field data. Such field-specific data may include        field names and identifiers, soil types or classifications,        tilling status, irrigation status.    -   5. A fifth set of data points includes field-specific data        related to historical harvest data. Such field-specific data may        include crop type or classification, harvest date, actual        production history (“APH”), yield, grain moisture, and tillage        practice, weather information (e.g., temperature, rainfall) to        the extent maintained or accessible by the user, previous        growing season information).    -   6. A sixth set of data points is field-specific data related to        soil composition. Such field-specific data may include        measurements of the acidity or basicity of soil (e.g., pH        levels), soil organic matter levels (“OM” levels), and cation        exchange capacity levels (“CEC” levels).

Humidity, temperature, and wind data, in the example embodiment, isextracted from short- and long-term forecast data (projected, future),regional and/or local sensor data (actual, present), and historicalhumidity and temperature data (past). Generally speaking, higherhumidity, lower temperature, and less wind will slow grain drying, whilelower humidity, higher temperature, and more wind will increase graindrying.

Accordingly, in the example embodiment, harvest advisor computing module158 computes one or more values associated with trace 3340, includingtarget harvest date 3346, based at least in part on the initial date(e.g., black layer date), the initial moisture value (e.g., the moisturevalue on the black layer date), the field condition data, and the targetharvest moisture value. More specifically, in one embodiment, trace 3340includes a plurality of data points P₀−P_(n). Each data point, P_(i),includes a date d_(i), and a moisture value m_(i). Some data points maybe actual values (e.g., inputted by user 110), while others may becomputed by harvest advisor computing module 158.

In the example embodiment, harvest advisor computing module 158 beginsto compute trace 3340 with an initial data point P₀={d₀=“9/8/14”,m₀=37%}, for example. Harvest advisor computing module 158 computes thenext day's data point, P_(i), for each subsequent day i based at leastin part on the previous day's data point, P_(i−1). Harvest advisorcomputing module 158 computes subsequent data points iteratively until,for example, the next day's moisture value, m_(i), is less than thetarget moisture value. The next day's moisture value, m_(i), is computedto be a delta amount, Δ_(i), less than the prior day's moisture value,m_(i−1). In other words:

m _(i) =m _(i−1)−Δ_(i).  (1)

Generally speaking, Δ_(i) represents the amount of moisture loss of thegrain over a single day (e.g., from day d_(i−1) to d_(i)), for example0.5% moisture loss per day. In the example embodiment, Δ_(i) isinfluenced by the field condition data (e.g., humidity, temperature,wind). In some embodiments, harvest advisor computing module 158 mayidentify a baseline, B, that includes one or more baseline environmentalconditions, such as a baseline humidity level, h′ (e.g., h′=80%), abaseline temperature, t′ (e.g., t′=76° Fahrenheit (F)), and a baselinewind speed, w′ (e.g., w′=4 miles per hour), as well as a baseline deltaamount, Δ′ (e.g., Δ′=0.5%). Generally speaking, baseline B thus definesa set of environmental conditions such that, if a current day i hasenvironmental conditions that match the baseline environmentalconditions (e.g., h′=80%, t′=76° F., and w′=4 miles per hour), then thegrain moisture value should decrease by the amount defined by Δ′ (e.g.,0.5%). In other words, if the current day's environmental conditionsmatch the baseline conditions, then:

Δ_(i)=Δ′,  (2)

and thus:

m _(i) =m _(i−1)−Δ_(i)  (3)

In the example embodiment, harvest advisor computing module 158 mayfurther adjust Δ_(i), for example when the current day, i, is projectedto experience, or actually experiences, environmental conditionsC_(i)={h_(i), t_(i), m_(i)} that do not match the baseline environmentalconditions (“environmental condition deviation”). For example, in someembodiments, an environmental condition deviation may adjust Δ′ by anamount, or by a weight, based on the difference between the currentcondition and the baseline condition. For example, harvest advisorcomputing module 158 may receive or otherwise identify a temperaturedeviation setting, Δt′, that indicates {±2 degrees F.=±0.02%}. In otherwords, harvest advisor computing module 158 alters Δ′ up 0.02% for every2 degrees F. the current temperature, t_(i), is above the baselinetemperature, t′. For example, presume t_(i)=80°, while current wind andhumidity match the baseline. A temperature deviation amount for thecurrent day, Δt_(i), is thus +0.04%. Harvest advisor computing module158 may thus compute the current day's delta amount as:

Δ_(i) =Δ′+Δt _(i).  (4)

Each environmental condition may alter the current day's delta amountsimilarly. Expressed more generally, in this embodiment, the currentday's delta amount may be expressed as:

Δ_(i) =Δ′+Δh _(i) +Δt _(i) +Δw _(i).  (5)

In other embodiments, harvest advisor computing module 158 determines adeviation weight, based on the baseline delta amount, Δ′, for one ormore environmental conditions. For example, harvest advisor computingmodule 158 may receive or otherwise identify a temperature deviationweight, Δt′, that indicates {±2 degrees F.=±0.1}. In other words,harvest advisor computing module 158 alters Δ′ up 0.1% of Δ′ for every 2degrees F. the current temperature, t_(i), is above the baselinetemperature, t′. For example, again presume t_(i)=80°, while currentwind and humidity match the baseline. A temperature deviation weight forthe current day, Δt_(i), is thus +0.2% of Δ′. Harvest advisor computingmodule 158 may thus compute the current day's delta amount as:

Δ_(i)=Δ′+(Δt _(i)·Δ′).  (6)

Each environmental condition may alter the current day's delta amountsimilarly. Expressed more generally, in this embodiment, the currentday's delta amount may be expressed as:

Δ_(i)=Δ′+(Δh _(i)·Δ′)+(Δt _(i)·Δ′)+(Δw _(i)·Δ′),  (7)

or

Δ_(i)=Δ′+(Δh _(i) +Δt _(i) +Δw _(i))·Δ′.  (8)

In some embodiments, user 110 may input and/or alter any one of thebaseline amounts, deviation settings, and/or any of the environmentalcondition delta amounts or weights. Further, in some embodiments,harvest advisor computing module 158 may compute baseline amounts fromactual historical data. For example, harvest advisor computing module158 may analyze past sensor data to determine the temperature deviationsetting. It should be understood that while the above examples describeenvironmental conditions of humidity, temperature, and wind as threefactors influencing the grain drying process prior to harvest, fewerthan all of these conditions may be used. Further, other environmentalconditions may be used such as, for example, environmental information,seed characteristic data, planting data, field data, historical harvestdata, and/or soil composition as described above in reference toproviding harvest recommendations.

As such, in the example embodiment, harvest advisor computing module 158computes a plurality of data points that represent projected grainmoisture values for the subject field. Further, in the exampleembodiment, harvest advisor computing module 158 computes a targetharvest date from the plurality of data points. More specifically, insome embodiments, harvest advisor computing module 158 identifies theearliest date (e.g., earliest d_(x)) that has a projected moisture value(e.g., m_(x)) nearest the target moisture value 3310 (e.g.,m_(x)≈16.0%). This earliest date, d_(x), is thus the computed targetharvest date (e.g., d_(x)=“Oct. 14”). Further, this point, P_(x), isdisplayed as target harvest date 3346, and serves as the beginning oftarget harvest window 3348. In other embodiments, as discussed above,P_(x) may represent a point somewhere within target harvest window 3348,as in the above example where target harvest window 3348 spans betweenmoisture values of 16.5% and 15.5%. Additionally, in the exampleembodiment, one or more of the target harvest date (e.g., d_(x)) and thetarget harvest window 3348 are displayed to user 110 as target harvestdate 3320.

In some embodiments, trace 3340 may include a plurality of actual valuessampled from the subject field. As such, other points within trace 3340may be interpolations between two known points, or actual values. Insome embodiments, the initial date may be a date after the black layerdate. For example, in one embodiment, the initial date is the date ofthe most recent moisture sample taken from the subject field, and thusthe initial moisture value is the sample moisture value taken at thatdate. In other words, harvest advisor computing module 158 may use themost recently sampled date from which to begin projecting futuremoisture values.

Although certain aspects of interfaces 3100, 3200, and 3300 as shown anddescribed in reference to FIGS. 31, 32, and 33, respectively, arespecific to corn crops, it should be understood that harvest advisorcomputing module 158 and interfaces 3100, 3200, and 3300 may becustomized for other crops (e.g., soy, wheat).

FIG. 34 is an example method 3400 for managing crop harvestingactivities of a crop growing in a field. In the example embodiment,method 3400 is performed by a computing system including at least oneprocessor and a memory, such as harvest advisor computing module 158(shown in FIG. 1) or harvest advisor computing module 425 (shown in FIG.4). In the example embodiment, method 3400 includes identifying 3410 aninitial date of a crop (e.g., black layer date) within a field. In someembodiments, identifying 3410 an initial date of a crop includes one ormore of (i) receiving a provided maturity date of the crop from the userand (ii) computing, by the processor, an estimated maturity date of thecrop based at least in part on one or more of a planting date of thecrop and field condition data.

In the example embodiment, method 3400 also includes identifying 3420 aninitial moisture value associated with the crop and the initial date(e.g., a sample moisture value collected on the black layer date). Insome embodiments, identifying 3420 an initial moisture value includesone or more of (i) receiving a provided moisture value of the crop onthe maturity date from the user and (ii) computing, by the processor, anestimated moisture value of the crop on the maturity date.

Method 3400, in the example embodiment, also includes identifying 3430 atarget harvest moisture value associated with the crop. Method 3400 alsoincludes receiving 3440 field condition data associated with the field.Method 3400 further includes computing 3450, by the harvest advisorcomputing module, a target harvest date for the crop based at least inpart on the initial date, the initial moisture value, the fieldcondition data, and the target harvest moisture value, wherein thetarget harvest date indicates a date at which the crop will have apresent moisture value approximately equal to the target harvestmoisture value. In some embodiments, computing 3450 a target harvestdate further includes computing the target harvest date based at leastin part on one or more of (i) estimated field condition data and (ii)actual field condition data for the field. Also, in some embodiments,computing 3750 a target harvest date further includes prompting the userto answer questions at the user device regarding one or more of (i)drydown costs, (ii) harvesting equipment (e.g., type, speed), (iii)harvesting equipment availability and location, labor costs, andexpected crop price. For certain questions, such as drydown costs,harvesting equipment, and labor costs, the user has the option toprovide a plurality of alternative responses such that harvest advisorcomputing module 158 may optimize recommendations. Further, in someembodiments, computing 3450 a target harvest date further includes oneor more of identifying a logistical feature including one or more of (i)a dry down cost, (ii) harvesting equipment availability, and (iii) fieldlocation data, and altering one or more of the target harvest date andthe additional target harvest date based at least in part on thelogistical feature.

In the example embodiment, method 3400 also includes displaying 3460 thetarget harvest date for the crop to the grower for harvest planning. Insome embodiments, method 3400 includes one or more of identifying anupcoming harvest risk near the target harvest date and displaying theupcoming harvest risk to the grower for harvest planning. In someembodiments, the crop is corn, the initial date is a black layer datefor the corn, and the initial moisture value and the target harvestmoisture value are moisture percentages. In some embodiments, method3400 also includes one or more of computing an additional target harvestdate for an additional crop on an additional field, computing a harvestplan for the field and the additional field based at least in part onthe harvest date and the additional harvest date, and providing theharvest plan to the grower for harvest planning.

As used herein, the term “non-transitory computer-readable media” isintended to be representative of any tangible computer-based deviceimplemented in any method or technology for short-term and long-termstorage of information, such as, computer-readable instructions, datastructures, program modules and sub-modules, or other data in anydevice. Therefore, the methods described herein may be encoded asexecutable instructions embodied in a tangible, non-transitory, computerreadable medium, including, without limitation, a storage device and/ora memory device. Such instructions, when executed by a processor, causethe processor to perform at least a portion of the methods describedherein. Moreover, as used herein, the term “non-transitorycomputer-readable media” includes all tangible, computer-readable media,including, without limitation, non-transitory computer storage devices,including, without limitation, volatile and nonvolatile media, andremovable and non-removable media such as a firmware, physical andvirtual storage, CD-ROMs, DVDs, and any other digital source such as anetwork or the Internet, as well as yet to be developed digital means,with the sole exception being a transitory, propagating signal.

This written description uses examples to disclose the disclosure,including the best mode, and also to enable any person skilled in theart to practice the embodiments, including making and using any devicesor systems and performing any incorporated methods. The patentable scopeof the disclosure is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal languages of the claims.

1.-24. (canceled)
 25. A method comprising: predicting a predicted targetharvest date for harvesting crop in the future, wherein the predictedtarget harvesting date is a date at which the harvesting crop will reacha moisture value approximately equal to a desired target harvestmoisture value; wherein the predicted target harvest date is predictedbased at least in part on a black layer date, an initial moisture value,field condition data, and desired target harvest moisture value;computing, by a computing device, a risk value that represents anumerical score and is determined based at least in part on the fieldcondition data and two or more of: pesticide application date, apesticide product type, a pesticide formulation, a pesticide usage rate,a pesticide amount sprayed, or a pesticide source identifier; updating,by the computing device, the predicted target harvest date based, atleast in part, on the computed risk value, wherein updating thepredicted target harvest date comprises updating a date of crop harvestfor which harvest yield of the harvesting crop is maximized; sending theupdated predicted target harvest date to a user device via a computernetwork connection established between the computing device and the userdevice; and displaying, by the user device, the updated predicted targetharvest date for the harvesting crop.
 26. The method of claim 25,further comprising: determining the predicted target harvest date basedat least in part on estimated field condition data for the field. 27.The method of claim 25, wherein reception of a black layer date of acrop includes one or more of (i) receiving a provided maturity date ofthe harvesting crop from the user device and (ii) computing, by aprocessor, an estimated maturity date of the harvesting crop based atleast in part on one or more of a planting date of the harvesting cropand field condition data.
 28. The method of claim 25, wherein receptionof an initial moisture value includes one or more of (i) receiving aprovided moisture value of the harvesting crop on a maturity date fromthe user device and (ii) computing, by the computing device, anestimated moisture value of the harvesting crop on the maturity date.29. The method of claim 25, further comprising: identifying an upcomingharvest risk near the predicted target harvest date; and displaying theupcoming harvest risk to the user device.
 30. The method of claim 25,wherein the harvesting crop is corn, wherein the black layer date is ablack layer date for the corn.
 31. The method of claim 25, furthercomprising: computing an additional predicted target harvest date for anadditional crop on an additional field; computing a harvest plan for thefield and the additional field based at least in part on the predictedtarget harvest date and the additional predicted target harvest date;and providing the harvest plan to the user device.
 32. The method ofclaim 31, wherein computing a harvest plan further includes: identifyinga logistical feature including one or more of (i) a dry down cost, (ii)harvesting equipment availability, and (iii) field location data; andaltering one or more of the predicted target harvest date and theadditional predicted target harvest date based at least in part on thelogistical feature.
 33. One or more non-transitory computer-readablestorage media storing computer-executable instructions which, whenexecuted by one or more processors, cause the one or more processors toperform: predicting a predicted target harvest date for harvesting cropin the future, wherein the predicted target harvesting date is a date atwhich the harvesting crop will reach a moisture value approximatelyequal to a desired target harvest moisture value; wherein the predictedtarget harvest date is predicted based at least in part on a black layerdate, an initial moisture value, field condition data, and desiredtarget harvest moisture value; computing, by a computing device, a riskvalue that represents a numerical score and is determined based at leastin part on the field condition data and two or more of: pesticideapplication date, a pesticide product type, a pesticide formulation, apesticide usage rate, a pesticide amount sprayed, or a pesticide sourceidentifier; updating, by the computing device, the predicted targetharvest date based, at least in part, on the computed risk value,wherein updating the predicted target harvest date comprises updating adate of crop harvest for which harvest yield of the harvesting crop ismaximized; sending the updated predicted target harvest date to a userdevice via a computer network connection established between thecomputing device and the user device; and displaying, by the userdevice, the updated predicted target harvest date for the harvestingcrop.
 34. The one or more non-transitory computer-readable storage mediain accordance with claim 33, wherein determining the predicted targetharvest date is based at least in part on estimated field condition datafor the field.
 35. The one or more non-transitory computer-readablestorage media in accordance with claim 33, wherein reception of a blacklayer date of a crop includes one or more of (i) receiving a providedmaturity date of the harvesting crop from the user device and (ii)computing an estimated maturity date of the harvesting crop based atleast in part on one or more of a planting date of the harvesting cropand field condition data.
 36. The one or more non-transitorycomputer-readable storage media in accordance with claim 33, whereinreception of an initial moisture value includes one or more of (i)receiving a provided moisture value of the harvesting crop on a maturitydate from the user device and (ii) computing, by the computing device,an estimated moisture value of the harvesting crop on the maturity date.37. The one or more non-transitory computer-readable storage media inaccordance with claim 33, storing additional instructions for:identifying an upcoming harvest risk near the predicted target harvestdate; and display the upcoming harvest risk to the user device.
 38. Theone or more non-transitory computer-readable storage media in accordancewith claim 33, wherein the harvesting crop is corn, wherein the blacklayer date is a black layer date for the corn.
 39. The one or morenon-transitory computer-readable storage media in accordance with claim33, storing additional instructions for: computing an additionalpredicted target harvest date for an additional crop on an additionalfield; computing a harvest plan for the field and the additional fieldbased at least in part on the predicted target harvest date and theadditional predicted target harvest date; and providing the harvest planto the user device.
 40. The one or more non-transitory computer-readablestorage media in accordance with claim 39, wherein computing a harvestplan further includes: identifying a logistical feature including one ormore of (i) a dry down cost, (ii) harvesting equipment availability, and(iii) field location data; and altering one or more of the predictedtarget harvest date and the additional predicted target harvest datebased at least in part on the logistical feature.